Systems and processes for improving quality and yield of one or more distillate products generated in a distillation column are disclosed. The system comprises a feed inlet distributor that reduces the amount of liquid entrained in vapor rising from the feed zone of the distillation column, a wash zone collection apparatus having an improved design for collecting slop wax falling from a liquid/vapor contacting structure provided in the wash zone, a recirculation subsystem for recirculating at least a portion of the collected slop wax to the top of the wash zone for distribution as wash oil, and a control subsystem. The feed inlet distributor ensures a horizontal fluid flow path free of transverse surfaces thereby minimizing atomization of liquid droplets entrained in vapor in the feed stream.

Split-shell fractionation columns and associated processes for separating aromatic hydrocarbons. A split-shell fractionation column includes a housing shell having a first height and a partition having a second height and disposed within the housing shell. The partition includes first and second vertically oriented baffles separated by a gap region, a seal plate connecting top ends of the baffles, a first input port formed to extend through the partition for the introduction of a gas into the gap region, and a first output port formed to extend outwardly from a bottom of the gap region and through the housing shell. The partition defines a first distillation zone and a second distillation zone within the housing shell.

A method includes performing a patterning step on a layer using a process gas. When the patterning step is performed, a signal strength is monitored, wherein the signal strength is from an emission spectrum of a compound generated from the patterning step. The compound includes an element in the patterned layer. At a time the signal strength is reduced to a pre-determined threshold value, the patterning step is stopped.

Processes and apparatuses for co-processing pyrolysis effluent and a hydrocarbon stream in which a char produced by the catalytic cracking of the pyrolysis effluent is recovered and utilized to provide energy, such as heat to the catalytic cracking zone. The char can be burned in various combustion zones associated with the catalytic cracking zone. The char is produced from a renewable resource.

Compositions having a high metal content comprising a metal salt solution, a stabilizer and one or more optional additives, wherein the metal salt solution comprises a metal ion, a counter ion and a solvent. The compositions are useful for forming films on substrates in the manufacture of solid state and integrated circuit devices.

A system for determining thickness variation values of a semiconductor substrate comprises a substrate vacuumed to a pedestal that defines a reference plane for measuring the substrate. A measurement probe assembly determines substrate CTV and BTV values, and defines a substrate slope angle. A thermal bonding assembly attaches a die to the substrate at a bonding angle congruent with the substrate slope angle. A plurality of substrates are measured using the same reference plane on the pedestal. Associated methods and processes are disclosed.

A gas generator and process for converting a fuel into an oxygen-depleted gas and/or hydrogen-enriched gas. The gas generator is preferably used for generating protection gas or reducing gas for start up, shut down or emergency shut down of a SOFC or SOEC. The process for converting fuel into oxygen-depleted gas and/or a hydrogen-enriched gas includes combusting the fuel in a primary catalytic burner with an oxygen-containing gas to produce a flue gas with oxygen, combusting or partially oxidizing the flue gas comprising oxygen with excess fuel in a secondary catalytic burner to produce a gas with hydrogen and carbon monoxide, and reducing the trace amounts of oxygen from the gas comprising traces of oxygen and obtaining an oxygen-depleted gas, or reducing the carbon monoxide present in the gas by conversion to carbon dioxide or methane to obtain a hydrogen-enriched gas.

Processes for the catalytic conversion of a carbonaceous composition into a gas stream comprising methane are provided. In addition, the processes provide for the generation of a hydrogen-enriched gas stream and, optionally, a carbon monoxide-enriched gas stream, which can be mixed or used separately as an energy source for subsequent catalytic gasification processes.

A solid feed system may comprise a supersonic nozzle, an isolated injection section having a port for injection of solid feedstock positioned downstream from the supersonic nozzle, and a supersonic diffuser positioned downstream from the isolated injection section. Additionally, a gasification system may comprise such a solid feed system and a reaction chamber downstream thereof. Furthermore, a method of reacting a solid feedstock under pressure may include directing a fluid flow through a supersonic nozzle to provide a supersonic flow stream, and directing the supersonic flow stream through an isolated injection section at a static pressure at least fifty percent (50%) lower than an operating pressure within a reaction chamber (e.g., at a static pressure near ambient pressure).

A process for fabricating an organic photodetector is presented. The process includes providing an array of thin film transistor assemblies, each thin film transistor assembly including a first electrode disposed on a thin film transistor; disposing an organic semiconductor layer on the array; disposing a second electrode layer including a first inorganic material on the organic semiconductor layer through a shadow mask to form a first etch stop layer; and removing portions of the organic semiconductor layer unprotected by the first etch stop layer using a dry etching process to form a multilayered structure. An organic photodetector, for example an organic x-ray detector fabricated by the process is further presented. An x-ray system including the organic x-ray detector is also presented.

This is the second part of a three-part series on use of litigation interest and risk assessment (LIRA), growing out of a program at CPR’s annual meeting in February 2020. The first part of this series describes how to do LIRAs and includes results from a survey of participants in our program.  This part describes … Continue reading Early Dispute Resolution Processes →

One thing you should know about me is that I am from New York and I am half Italian. That means when I like something,…

New Publication: Rules, Procedures and Mechanisms Applicable to Processes under the Cartagena Protocol on Biosafety.

Spinout founders, investors and insiders give their tips on turning academic research into successful businesses

D. V. Gusak and E. V. Karnaukh
Theor. Probability and Math. Statist. 99 (2020), 77-89.
Abstract, references and article information

The developing nervous system is remarkably sensitive to environmental signals, including disruptive toxins, such as polybrominated diphenyl ethers (PBDEs). PBDEs are an environmentally pervasive class of brominated flame retardants whose neurodevelopmental toxicity mechanisms remain largely unclear. Using dissociated cortical neurons from embryonic Rattus norvegicus, we found here that chronic exposure to 6-OH–BDE-47, one of the most prevalent hydroxylated PBDE metabolites, suppresses both spontaneous and evoked neuronal electrical activity. On the basis of our previous work on mitogen-activated protein kinase (MAPK)/extracellular signal-related kinase (ERK) (MEK) biology and our observation that 6-OH–BDE-47 is structurally similar to kinase inhibitors, we hypothesized that certain hydroxylated PBDEs mediate neurotoxicity, at least in part, by impairing the MEK–ERK axis of MAPK signal transduction. We tested this hypothesis on three experimental platforms: 1) in silico, where modeling ligand–protein docking suggested that 6-OH–BDE-47 is a promiscuous ATP-competitive kinase inhibitor; 2) in vitro in dissociated neurons, where 6-OH–BDE-47 and another specific hydroxylated BDE metabolite similarly impaired phosphorylation of MEK/ERK1/2 and activity-induced transcription of a neuronal immediate early gene; and 3) in vivo in Drosophila melanogaster, where developmental exposures to 6-OH–BDE-47 and a MAPK inhibitor resulted in offspring displaying similarly increased frequency of mushroom-body β–lobe midline crossing, a metric of axonal guidance. Taken together, our results support that certain ortho-hydroxylated PBDE metabolites are promiscuous kinase inhibitors and can cause disruptions of critical neurodevelopmental processes, including neuronal electrical activity, pre-synaptic functions, MEK–ERK signaling, and axonal guidance.

The developing nervous system is remarkably sensitive to environmental signals, including disruptive toxins, such as polybrominated diphenyl ethers (PBDEs). PBDEs are an environmentally pervasive class of brominated flame retardants whose neurodevelopmental toxicity mechanisms remain largely unclear. Using dissociated cortical neurons from embryonic Rattus norvegicus, we found here that chronic exposure to 6-OH–BDE-47, one of the most prevalent hydroxylated PBDE metabolites, suppresses both spontaneous and evoked neuronal electrical activity. On the basis of our previous work on mitogen-activated protein kinase (MAPK)/extracellular signal-related kinase (ERK) (MEK) biology and our observation that 6-OH–BDE-47 is structurally similar to kinase inhibitors, we hypothesized that certain hydroxylated PBDEs mediate neurotoxicity, at least in part, by impairing the MEK–ERK axis of MAPK signal transduction. We tested this hypothesis on three experimental platforms: 1) in silico, where modeling ligand–protein docking suggested that 6-OH–BDE-47 is a promiscuous ATP-competitive kinase inhibitor; 2) in vitro in dissociated neurons, where 6-OH–BDE-47 and another specific hydroxylated BDE metabolite similarly impaired phosphorylation of MEK/ERK1/2 and activity-induced transcription of a neuronal immediate early gene; and 3) in vivo in Drosophila melanogaster, where developmental exposures to 6-OH–BDE-47 and a MAPK inhibitor resulted in offspring displaying similarly increased frequency of mushroom-body β–lobe midline crossing, a metric of axonal guidance. Taken together, our results support that certain ortho-hydroxylated PBDE metabolites are promiscuous kinase inhibitors and can cause disruptions of critical neurodevelopmental processes, including neuronal electrical activity, pre-synaptic functions, MEK–ERK signaling, and axonal guidance.

The developing nervous system is remarkably sensitive to environmental signals, including disruptive toxins, such as polybrominated diphenyl ethers (PBDEs). PBDEs are an environmentally pervasive class of brominated flame retardants whose neurodevelopmental toxicity mechanisms remain largely unclear. Using dissociated cortical neurons from embryonic Rattus norvegicus, we found here that chronic exposure to 6-OH–BDE-47, one of the most prevalent hydroxylated PBDE metabolites, suppresses both spontaneous and evoked neuronal electrical activity. On the basis of our previous work on mitogen-activated protein kinase (MAPK)/extracellular signal-related kinase (ERK) (MEK) biology and our observation that 6-OH–BDE-47 is structurally similar to kinase inhibitors, we hypothesized that certain hydroxylated PBDEs mediate neurotoxicity, at least in part, by impairing the MEK–ERK axis of MAPK signal transduction. We tested this hypothesis on three experimental platforms: 1) in silico, where modeling ligand–protein docking suggested that 6-OH–BDE-47 is a promiscuous ATP-competitive kinase inhibitor; 2) in vitro in dissociated neurons, where 6-OH–BDE-47 and another specific hydroxylated BDE metabolite similarly impaired phosphorylation of MEK/ERK1/2 and activity-induced transcription of a neuronal immediate early gene; and 3) in vivo in Drosophila melanogaster, where developmental exposures to 6-OH–BDE-47 and a MAPK inhibitor resulted in offspring displaying similarly increased frequency of mushroom-body β–lobe midline crossing, a metric of axonal guidance. Taken together, our results support that certain ortho-hydroxylated PBDE metabolites are promiscuous kinase inhibitors and can cause disruptions of critical neurodevelopmental processes, including neuronal electrical activity, pre-synaptic functions, MEK–ERK signaling, and axonal guidance.

François Bachoc, José Betancourt, Reinhard Furrer, Thierry Klein.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 1962--2008.

Abstract:
The asymptotic analysis of covariance parameter estimation of Gaussian processes has been subject to intensive investigation. However, this asymptotic analysis is very scarce for non-Gaussian processes. In this paper, we study a class of non-Gaussian processes obtained by regular non-linear transformations of Gaussian processes. We provide the increasing-domain asymptotic properties of the (Gaussian) maximum likelihood and cross validation estimators of the covariance parameters of a non-Gaussian process of this class. We show that these estimators are consistent and asymptotically normal, although they are defined as if the process was Gaussian. They do not need to model or estimate the non-linear transformation. Our results can thus be interpreted as a robustness of (Gaussian) maximum likelihood and cross validation towards non-Gaussianity. Our proofs rely on two technical results that are of independent interest for the increasing-domain asymptotic literature of spatial processes. First, we show that, under mild assumptions, coefficients of inverses of large covariance matrices decay at an inverse polynomial rate as a function of the corresponding observation location distances. Second, we provide a general central limit theorem for quadratic forms obtained from transformed Gaussian processes. Finally, our asymptotic results are illustrated by numerical simulations.

Claudia Wehrhahn, Samuel Leonard, Abel Rodriguez, Tatiana Xifara.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 1449--1478.

Abstract:
Identifying disease clusters (areas with an unusually high incidence of a particular disease) is a common problem in epidemiology and public health. We describe a Bayesian nonparametric mixture model for disease clustering that constrains clusters to be made of adjacent areal units. This is achieved by modifying the exchangeable partition probability function associated with the Ewen’s sampling distribution. We call the resulting prior the Restricted Chinese Restaurant Process, as the associated full conditional distributions resemble those associated with the standard Chinese Restaurant Process. The model is illustrated using synthetic data sets and in an application to oral cancer mortality in Germany.

Christophe A. N. Biscio, Nicolas Chenavier, Christian Hirsch, Anne Marie Svane.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 1024--1074.

Abstract:
We introduce tests for the goodness of fit of point patterns via methods from topological data analysis. More precisely, the persistent Betti numbers give rise to a bivariate functional summary statistic for observed point patterns that is asymptotically Gaussian in large observation windows. We analyze the power of tests derived from this statistic on simulated point patterns and compare its performance with global envelope tests. Finally, we apply the tests to a point pattern from an application context in neuroscience. As the main methodological contribution, we derive sufficient conditions for a functional central limit theorem on bounded persistent Betti numbers of point processes with exponential decay of correlations.

We consider the problem of unveiling the implicit network structure of node interactions (such as user interactions in a social network), based only on high-frequency timestamps. Our inference is based on the minimization of the least-squares loss associated with a multivariate Hawkes model, penalized by $ell_1$ and trace norm of the interaction tensor. We provide a first theoretical analysis for this problem, that includes sparsity and low-rank inducing penalizations. This result involves a new data-driven concentration inequality for matrix martingales in continuous time with observable variance, which is a result of independent interest and a broad range of possible applications since it extends to matrix martingales former results restricted to the scalar case. A consequence of our analysis is the construction of sharply tuned $ell_1$ and trace-norm penalizations, that leads to a data-driven scaling of the variability of information available for each users. Numerical experiments illustrate the significant improvements achieved by the use of such data-driven penalizations.

Mohsen Maleki, Arezo Hajrajabi, Reinaldo B. Arellano-Valle.

Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 2, 273--290.

Abstract:
In this paper, we study the finite mixtures of autoregressive processes assuming that the distribution of innovations (errors) belongs to the class of scale mixture of skew-normal (SMSN) distributions. The SMSN distributions allow a simultaneous modeling of the existence of outliers, heavy tails and asymmetries in the distribution of innovations. Therefore, a statistical methodology based on the SMSN family allows us to use a robust modeling on some non-linear time series with great flexibility, to accommodate skewness, heavy tails and heterogeneity simultaneously. The existence of convenient hierarchical representations of the SMSN distributions facilitates also the implementation of an ECME-type of algorithm to perform the likelihood inference in the considered model. Simulation studies and the application to a real data set are finally presented to illustrate the usefulness of the proposed model.

Dani Gamerman.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 4, 686--705.

Abstract:
Point processes are one of the most commonly encountered observation processes in Spatial Statistics. Model-based inference for them depends on the likelihood function. In the most standard setting of Poisson processes, the likelihood depends on the intensity function, and can not be computed analytically. A number of approximating techniques have been proposed to handle this difficulty. In this paper, we review recent work on exact solutions that solve this problem without resorting to approximations. The presentation concentrates more heavily on discrete time but also considers continuous time. The solutions are based on model specifications that impose smoothness constraints on the intensity function. We also review approaches to include a regression component and different ways to accommodate it while accounting for additional heterogeneity. Applications are provided to illustrate the results. Finally, we discuss possible extensions to account for discontinuities and/or jumps in the intensity function.

Rodrigo Citton P. dos Reis, Enrico A. Colosimo, Gustavo L. Gilardoni.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 2, 374--396.

Abstract:
In the data analysis from multiple repairable systems, it is usual to observe both different truncation times and heterogeneity among the systems. Among other reasons, the latter is caused by different manufacturing lines and maintenance teams of the systems. In this paper, a hierarchical model is proposed for the statistical analysis of multiple repairable systems under different truncation times. A reparameterization of the power law process is proposed in order to obtain a quasi-conjugate bayesian analysis. An empirical Bayes approach is used to estimate model hyperparameters. The uncertainty in the estimate of these quantities are corrected by using a parametric bootstrap approach. The results are illustrated in a real data set of failure times of power transformers from an electric company in Brazil.

Yong-Chao Zhang, Na Zhang.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 1, 184--191.

Abstract:
We investigate the equivalence of dynamic and static asset allocations in the case where the price process of a risky asset is driven by a Poisson process. Under some mild conditions, we obtain a necessary and sufficient condition for the equivalence of dynamic and static asset allocations. In addition, we provide a simple sufficient condition for the equivalence.

Holger Dette, Weichi Wu.

Source: The Annals of Statistics, Volume 47, Number 6, 3578--3608.

Abstract:
This paper considers the problem of testing if a sequence of means $(mu_{t})_{t=1,ldots ,n}$ of a nonstationary time series $(X_{t})_{t=1,ldots ,n}$ is stable in the sense that the difference of the means $mu_{1}$ and $mu_{t}$ between the initial time $t=1$ and any other time is smaller than a given threshold, that is $|mu_{1}-mu_{t}|leq c$ for all $t=1,ldots ,n$. A test for hypotheses of this type is developed using a bias corrected monotone rearranged local linear estimator and asymptotic normality of the corresponding test statistic is established. As the asymptotic variance depends on the location of the roots of the equation $|mu_{1}-mu_{t}|=c$ a new bootstrap procedure is proposed to obtain critical values and its consistency is established. As a consequence we are able to quantitatively describe relevant deviations of a nonstationary sequence from its initial value. The results are illustrated by means of a simulation study and by analyzing data examples.

Steffen Grønneberg, Benjamin Holcblat.

Source: The Annals of Statistics, Volume 47, Number 6, 3216--3243.

Abstract:
We establish general and versatile results regarding the limit behavior of the partial-sum process of ARMAX residuals. Illustrations include ARMA with seasonal dummies, misspecified ARMAX models with autocorrelated errors, nonlinear ARMAX models, ARMA with a structural break, a wide range of ARMAX models with infinite-variance errors, weak GARCH models and the consistency of kernel estimation of the density of ARMAX errors. Our results identify the limit distributions, and provide a general algorithm to obtain pivot statistics for CUSUM tests.

Stefan Richter, Rainer Dahlhaus.

Source: The Annals of Statistics, Volume 47, Number 4, 2145--2173.

Abstract:
We propose an adaptive bandwidth selector via cross validation for local M-estimators in locally stationary processes. We prove asymptotic optimality of the procedure under mild conditions on the underlying parameter curves. The results are applicable to a wide range of locally stationary processes such linear and nonlinear processes. A simulation study shows that the method works fairly well also in misspecified situations.

Medha Uppala, Mark S. Handcock.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 339--356.

Abstract:
This paper focuses on spatial and temporal modeling of point processes on linear networks. Point processes on linear networks can simply be defined as point events occurring on or near line segment network structures embedded in a certain space. A separable modeling framework is introduced that posits separate formation and dissolution models of point processes on linear networks over time. While the model was inspired by spider web building activity in brick mortar lines, the focus is on modeling wildfire ignition origins near road networks over a span of 14 years. As most wildfires in California have human-related origins, modeling the origin locations with respect to the road network provides insight into how human, vehicular and structural densities affect ignition occurrence. Model results show that roads that traverse different types of regions such as residential, interface and wildland regions have higher ignition intensities compared to roads that only exist in each of the mentioned region types.

Jason Xu, Samson Koelle, Peter Guttorp, Chuanfeng Wu, Cynthia Dunbar, Janis L. Abkowitz, Vladimir N. Minin.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2091--2119.

Abstract:
Single-cell lineage tracking strategies enabled by recent experimental technologies have produced significant insights into cell fate decisions, but lack the quantitative framework necessary for rigorous statistical analysis of mechanistic models describing cell division and differentiation. In this paper, we develop such a framework with corresponding moment-based parameter estimation techniques for continuous-time, multi-type branching processes. Such processes provide a probabilistic model of how cells divide and differentiate, and we apply our method to study hematopoiesis , the mechanism of blood cell production. We derive closed-form expressions for higher moments in a general class of such models. These analytical results allow us to efficiently estimate parameters of much richer statistical models of hematopoiesis than those used in previous statistical studies. To our knowledge, the method provides the first rate inference procedure for fitting such models to time series data generated from cellular barcoding experiments. After validating the methodology in simulation studies, we apply our estimator to hematopoietic lineage tracking data from rhesus macaques. Our analysis provides a more complete understanding of cell fate decisions during hematopoiesis in nonhuman primates, which may be more relevant to human biology and clinical strategies than previous findings from murine studies. For example, in addition to previously estimated hematopoietic stem cell self-renewal rate, we are able to estimate fate decision probabilities and to compare structurally distinct models of hematopoiesis using cross validation. These estimates of fate decision probabilities and our model selection results should help biologists compare competing hypotheses about how progenitor cells differentiate. The methodology is transferrable to a large class of stochastic compartmental and multi-type branching models, commonly used in studies of cancer progression, epidemiology and many other fields.

Sarat B. Moka, Dirk P. Kroese.

Source: Bernoulli, Volume 26, Number 3, 2082--2104.

Abstract:
We present a perfect sampling algorithm for Gibbs point processes, based on the partial rejection sampling of Guo, Jerrum and Liu (In STOC’17 – Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing (2017) 342–355 ACM). Our particular focus is on pairwise interaction processes, penetrable spheres mixture models and area-interaction processes, with a finite interaction range. For an interaction range $2r$ of the target process, the proposed algorithm can generate a perfect sample with $O(log(1/r))$ expected running time complexity, provided that the intensity of the points is not too high and $Theta(1/r^{d})$ parallel processor units are available.

Martin Kolb, Mladen Savov.

Source: Bernoulli, Volume 26, Number 2, 1453--1472.

Abstract:
We derive a criterium for the almost sure finiteness of perpetual integrals of Lévy processes for a class of real functions including all continuous functions and for general one-dimensional Lévy processes that drifts to plus infinity. This generalizes previous work of Döring and Kyprianou, who considered Lévy processes having a local time, leaving the general case as an open problem. It turns out, that the criterium in the general situation simplifies significantly in the situation, where the process has a local time, but we also demonstrate that in general our criterium can not be reduced. This answers an open problem posed in ( J. Theoret. Probab. 29 (2016) 1192–1198).

Pasha Tkachov.

Source: Bernoulli, Volume 26, Number 2, 1354--1380.

Abstract:
The aim of this paper is to prove stability of traveling waves for integro-differential equations connected with branching Markov processes. In other words, the limiting law of the left-most particle of a (time-continuous) branching Markov process with a Lévy non-branching part is demonstrated. The key idea is to approximate the branching Markov process by a branching random walk and apply the result of Aïdékon [ Ann. Probab. 41 (2013) 1362–1426] on the limiting law of the latter one.

Giacomo Aletti, Irene Crimaldi, Andrea Ghiglietti.

Source: Bernoulli, Volume 26, Number 2, 1098--1138.

Abstract:
This work deals with a system of interacting reinforced stochastic processes , where each process $X^{j}=(X_{n,j})_{n}$ is located at a vertex $j$ of a finite weighted directed graph, and it can be interpreted as the sequence of “actions” adopted by an agent $j$ of the network. The interaction among the dynamics of these processes depends on the weighted adjacency matrix $W$ associated to the underlying graph: indeed, the probability that an agent $j$ chooses a certain action depends on its personal “inclination” $Z_{n,j}$ and on the inclinations $Z_{n,h}$, with $h eq j$, of the other agents according to the entries of $W$. The best known example of reinforced stochastic process is the Pólya urn. The present paper focuses on the weighted empirical means $N_{n,j}=sum_{k=1}^{n}q_{n,k}X_{k,j}$, since, for example, the current experience is more important than the past one in reinforced learning. Their almost sure synchronization and some central limit theorems in the sense of stable convergence are proven. The new approach with weighted means highlights the key points in proving some recent results for the personal inclinations $Z^{j}=(Z_{n,j})_{n}$ and for the empirical means $overline{X}^{j}=(sum_{k=1}^{n}X_{k,j}/n)_{n}$ given in recent papers (e.g. Aletti, Crimaldi and Ghiglietti (2019), Ann. Appl. Probab. 27 (2017) 3787–3844, Crimaldi et al. Stochastic Process. Appl. 129 (2019) 70–101). In fact, with a more sophisticated decomposition of the considered processes, we can understand how the different convergence rates of the involved stochastic processes combine. From an application point of view, we provide confidence intervals for the common limit inclination of the agents and a test statistics to make inference on the matrix $W$, based on the weighted empirical means. In particular, we answer a research question posed in Aletti, Crimaldi and Ghiglietti (2019).

Leif Döring, Philip Weissmann.

Source: Bernoulli, Volume 26, Number 2, 980--1015.

Abstract:
Conditioning stable Lévy processes on zero probability events recently became a tractable subject since several explicit formulas emerged from a deep analysis using the Lamperti transformations for self-similar Markov processes. In this article, we derive new harmonic functions and use them to explain how to condition stable processes to hit continuously a compact interval from the outside.

Jie Yen Fan, Kais Hamza, Peter Jagers, Fima Klebaner.

Source: Bernoulli, Volume 26, Number 2, 893--926.

Abstract:
We consider a family of general branching processes with reproduction parameters depending on the age of the individual as well as the population age structure and a parameter $K$, which may represent the carrying capacity. These processes are Markovian in the age structure. In a previous paper ( Proc. Steklov Inst. Math. 282 (2013) 90–105), the Law of Large Numbers as $K o infty $ was derived. Here we prove the central limit theorem, namely the weak convergence of the fluctuation processes in an appropriate Skorokhod space. We also show that the limit is driven by a stochastic partial differential equation.

Richard A. Davis, Mikkel Slot Nielsen, Victor Rohde.

Source: Bernoulli, Volume 26, Number 2, 799--827.

Abstract:
In this paper, we introduce a model, the stochastic fractional delay differential equation (SFDDE), which is based on the linear stochastic delay differential equation and produces stationary processes with hyperbolically decaying autocovariance functions. The model departs from the usual way of incorporating this type of long-range dependence into a short-memory model as it is obtained by applying a fractional filter to the drift term rather than to the noise term. The advantages of this approach are that the corresponding long-range dependent solutions are semimartingales and the local behavior of the sample paths is unaffected by the degree of long memory. We prove existence and uniqueness of solutions to the SFDDEs and study their spectral densities and autocovariance functions. Moreover, we define a subclass of SFDDEs which we study in detail and relate to the well-known fractionally integrated CARMA processes. Finally, we consider the task of simulating from the defining SFDDEs.

Tobias Zwingmann, Hajo Holzmann.

Source: Bernoulli, Volume 26, Number 1, 323--351.

Abstract:
We show weak convergence of quantile and expectile processes to Gaussian limit processes in the space of bounded functions endowed with an appropriate semimetric which is based on the concepts of epi- and hypo- convergence as introduced in A. Bücher, J. Segers and S. Volgushev (2014), ‘ When Uniform Weak Convergence Fails: Empirical Processes for Dependence Functions and Residuals via Epi- and Hypographs ’, Annals of Statistics 42 . We impose assumptions for which it is known that weak convergence with respect to the supremum norm generally fails to hold. For quantiles, we consider stationary observations, where the marginal distribution function is assumed to be strictly increasing and continuous except for finitely many points and to admit strictly positive – possibly infinite – left- and right-sided derivatives. For expectiles, we focus on independent and identically distributed (i.i.d.) observations. Only a finite second moment and continuity at the boundary points but no further smoothness properties of the distribution function are required. We also show consistency of the bootstrap for this mode of convergence in the i.i.d. case for quantiles and expectiles.

Jarno Vanhatalo, Marcelo Hartmann, Lari Veneranta.

Source: Bayesian Analysis, Volume 15, Number 2, 415--447.

Abstract:
Species distribution models (SDM) are a key tool in ecology, conservation and management of natural resources. Two key components of the state-of-the-art SDMs are the description for species distribution response along environmental covariates and the spatial random effect that captures deviations from the distribution patterns explained by environmental covariates. Joint species distribution models (JSDMs) additionally include interspecific correlations which have been shown to improve their descriptive and predictive performance compared to single species models. However, current JSDMs are restricted to hierarchical generalized linear modeling framework. Their limitation is that parametric models have trouble in explaining changes in abundance due, for example, highly non-linear physical tolerance limits which is particularly important when predicting species distribution in new areas or under scenarios of environmental change. On the other hand, semi-parametric response functions have been shown to improve the predictive performance of SDMs in these tasks in single species models. Here, we propose JSDMs where the responses to environmental covariates are modeled with additive multivariate Gaussian processes coded as linear models of coregionalization. These allow inference for wide range of functional forms and interspecific correlations between the responses. We propose also an efficient approach for inference with Laplace approximation and parameterization of the interspecific covariance matrices on the Euclidean space. We demonstrate the benefits of our model with two small scale examples and one real world case study. We use cross-validation to compare the proposed model to analogous semi-parametric single species models and parametric single and joint species models in interpolation and extrapolation tasks. The proposed model outperforms the alternative models in all cases. We also show that the proposed model can be seen as an extension of the current state-of-the-art JSDMs to semi-parametric models.

Guillaume Kon Kam King, Antonio Canale, Matteo Ruggiero.

Source: Bayesian Analysis, Volume 14, Number 4, 1121--1141.

Abstract:
Motivated by the problem of forecasting demand and offer curves, we introduce a class of nonparametric dynamic models with locally-autoregressive behaviour, and provide a full inferential strategy for forecasting time series of piecewise-constant non-decreasing functions over arbitrary time horizons. The model is induced by a non Markovian system of interacting particles whose evolution is governed by a resampling step and a drift mechanism. The former is based on a global interaction and accounts for the volatility of the functional time series, while the latter is determined by a neighbourhood-based interaction with the past curves and accounts for local trend behaviours, separating these from pure noise. We discuss the implementation of the model for functional forecasting by combining a population Monte Carlo and a semi-automatic learning approach to approximate Bayesian computation which require limited tuning. We validate the inference method with a simulation study, and carry out predictive inference on a real dataset on the Italian natural gas market.

Lizhen Lin, Niu Mu, Pokman Cheung, David Dunson.

Source: Bayesian Analysis, Volume 14, Number 3, 907--926.

Abstract:
Gaussian processes (GPs) are very widely used for modeling of unknown functions or surfaces in applications ranging from regression to classification to spatial processes. Although there is an increasingly vast literature on applications, methods, theory and algorithms related to GPs, the overwhelming majority of this literature focuses on the case in which the input domain corresponds to a Euclidean space. However, particularly in recent years with the increasing collection of complex data, it is commonly the case that the input domain does not have such a simple form. For example, it is common for the inputs to be restricted to a non-Euclidean manifold, a case which forms the motivation for this article. In particular, we propose a general extrinsic framework for GP modeling on manifolds, which relies on embedding of the manifold into a Euclidean space and then constructing extrinsic kernels for GPs on their images. These extrinsic Gaussian processes (eGPs) are used as prior distributions for unknown functions in Bayesian inferences. Our approach is simple and general, and we show that the eGPs inherit fine theoretical properties from GP models in Euclidean spaces. We consider applications of our models to regression and classification problems with predictors lying in a large class of manifolds, including spheres, planar shape spaces, a space of positive definite matrices, and Grassmannians. Our models can be readily used by practitioners in biological sciences for various regression and classification problems, such as disease diagnosis or detection. Our work is also likely to have impact in spatial statistics when spatial locations are on the sphere or other geometric spaces.

Lloyd T. Elliott, Maria De Iorio, Stefano Favaro, Kaustubh Adhikari, Yee Whye Teh.

Source: Bayesian Analysis, Volume 14, Number 2, 313--339.

Abstract:
We propose a Bayesian nonparametric model to infer population admixture, extending the hierarchical Dirichlet process to allow for correlation between loci due to linkage disequilibrium. Given multilocus genotype data from a sample of individuals, the proposed model allows inferring and classifying individuals as unadmixed or admixed, inferring the number of subpopulations ancestral to an admixed population and the population of origin of chromosomal regions. Our model does not assume any specific mutation process, and can be applied to most of the commonly used genetic markers. We present a Markov chain Monte Carlo (MCMC) algorithm to perform posterior inference from the model and we discuss some methods to summarize the MCMC output for the analysis of population admixture. Finally, we demonstrate the performance of the proposed model in a real application, using genetic data from the ectodysplasin-A receptor (EDAR) gene, which is considered to be ancestry-informative due to well-known variations in allele frequency as well as phenotypic effects across ancestry. The structure analysis of this dataset leads to the identification of a rare haplotype in Europeans. We also conduct a simulated experiment and show that our algorithm outperforms parametric methods.

Georg Lindgren.

Source: Statistical Science, Volume 34, Number 1, 100--128.

Abstract:
We describe and compare how methods based on the classical Rice’s formula for the expected number, and higher moments, of level crossings by a Gaussian process stand up to contemporary numerical methods to accurately deal with crossing related characteristics of the sample paths. We illustrate the relative merits in accuracy and computing time of the Rice moment methods and the exact numerical method, developed since the late 1990s, on three groups of distribution problems, the maximum over a finite interval and the waiting time to first crossing, the length of excursions over a level, and the joint period/amplitude of oscillations. We also treat the notoriously difficult problem of dependence between successive zero crossing distances. The exact solution has been known since at least 2000, but it has remained largely unnoticed outside the ocean science community. Extensive simulation studies illustrate the accuracy of the numerical methods. As a historical introduction an attempt is made to illustrate the relation between Rice’s original formulation and arguments and the exact numerical methods.

Previous studies examining developmental outcomes associated with late preterm and early term birth have shown mixed results. Many of these studies did not fully take into account the role of the social environment in child development.

Social factors, not late preterm or early term birth, were the strongest predictors of poor developmental outcomes at 2 to 3 and 4 to 5 years. The influence of mild prematurity may lose strength beyond the neonatal period. (Read the full article)

Public sector bodies face many challenges as a result of the outbreak of coronavirus.  Contracting Authorities may need to procure goods, services or works urgently to deal with these challenges.  Challenges caused by the outbreak may be e...