If this were Britain, Russia or India, Rudy Giuliani '08 caps would not be on the clearance racks. In those countries, where bigwigs and insiders get to nominate party leaders, the former Republican front-runner and establishment favorite would have long ago been anointed the winner.

People hate Mondays. And they love Fridays. The Carpenters crooned about being blue in "Rainy Days and Mondays." The restaurant chain T.G.I. Friday's might restrict its clientele to workaholics if it were to rename itself T.G.I. Monday's.

This podcast features a discussion between MPI's Margie McHugh and Julie Sugarman about how to understand the varying composition of states' English Learner (EL) subgroup under ESSA, and why understanding these technical differences matters when making decisions about how ELs and schools are faring. They also talk about different groups of ELs: newcomers, students with interrupted formal education, and long-term ELs, and data collection around these different cohorts.

Governments are facing urgent pandemic-related questions. One of the more pressing ones: Who is going to harvest crops in countries that rely heavily on seasonal foreign workers? In this podcast, MPI experts examine ways in which countries could address labor shortages in agriculture, including recruiting native-born workers and letting already present seasonal workers stay longer. Catch an interesting discussion as border closures have halted the movement of seasonal workers even as crops are approaching harvest in some places.

In this webinar, the authors of three papers on the experiences of refugee children present their findings, with a focus on how such experiences affect their mental health and education.

According to leaked drafts, the Trump administration is considering a rule that could have sweeping effects on both legal immigration to the United States and the use of public benefits by legal immigrants and their families. This report examines the potential scale of the expected rule’s impact, including at national and state levels and among children, as well as Hispanic and Asian American/Pacific Islander immigrants.

This webinar highlights findings from an MPI report examining the potential impacts of expected changes to the public charge rule by the Trump administration. Leaked draft versions suggest the rule could sharply expand the number of legally present noncitizens facing difficulty getting a green card or extending a visa as a result of their family's use of public benefits. The rule likely would discourage millions from accessing health, nutrition, and social services for which they or their U.S.-citizen dependents are eligible.

A Trump administration “public-charge” rule expected to be unveiled soon could create the potential to significantly reshape family-based legal immigration to the United States—and reduce arrivals from Asia, Latin America, and Africa—by imposing a de facto financial test that 40 percent of the U.S. born themselves would fail, as this commentary explains.

Paul Ciechanowski
Apr 1, 2011; 29:43-49
Feature Articles

Restaurant guest experience depends on more than good food and quick service.

The post Customer experience tweaks that boost restaurant results appeared first on Neuromarketing.

Most research on people who patronize sex workers has focused on men. In some ways, this isn’t surprising because men are much more likely to report having paid for sex than are women. For example, in a recent YouGov survey of 1,000 adult Americans, 12% of men reported having paid for sex before compared to just 1% of women. Similarly, in a nationally representative survey of more than 20,000 Australians aged 16-69, researchers found that 17% of men said they had paid for sex, while only 0.3% of women said the same [1]. However, these figures may significantly underreport the actual number of women who have ever engaged the services of a sex worker.

Ohio Gov. John Kasich is expected to sign a bill into law that would ban suspensions and expulsions for children in prekindergarten through 3rd grade for minor offenses.

“Detracking” math teachers is tough because many educators resist upending their routines or challenging informal hierarchies, and PD initiatives to make it happen are limited.

A group of teachers and literacy advocates are pushing back after Richard Allington, one of the country's most prominent experts on early literacy, made inflammatory claims about dyslexia at a Tennessee literacy conference this week.

“Detracking” math teachers is tough because many educators resist upending their routines or challenging informal hierarchies, and PD initiatives to make it happen are limited.

Five of the 12 schools in the Pac-12 expect to reopen their campuses this fall, a key step to the return of college sports. The football season begins Aug. 29 with a slate of games that include three Pac-12 schools. Both Arizona schools, both Washington schools and Oregon anticipate holding in-person classes in the fall, but that leaves seven others still mulling whether to follow suit or continue holding online classes.

All but one of the 14 schools in the Southeastern Conference have indicated they plan to reopen their campuses for the fall semester, a step widely believed to be needed to resume football and other sports. ''We will follow clear public health protocols, including social distancing within classrooms, lecture halls, meeting rooms and sports venues, with strong encouragement of proper social distancing off campus,'' he said. The commissioners of the nation's major college football leagues have stressed that college sports cannot return from the shutdown until campuses have reopened.

Ability -- United States.

Magic.

London ; New York : Ward, Lock and Co., 1891.

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Berlin : A. Hirschwald, 1865.

Wien : L.W. Seidel, 1887.

Stuttgart : Enke, 1897.

Stuttgart : F. Enke, 1892.

Berlin : S. Karger, 1899.

Breslau : W. Koebner, 1888.

London : printed for Knight and Lacey, 1826.

London : Longmans, Green, 1868.

Ron DeSantis had a thin record on K-12 issues as a Florida congressman, but as a first-term Republican governor he’s pushed an aggressive agenda on issues such as vouchers, teacher salaries and bonus pay, and even the common core.

Rockville, Maryland : National Institute on Drug Abuse, 1978.

Tuo Chen, Zhihua Su, Yi Yang, Shanshan Ding.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 143--173.

Abstract:
As a generalization of the classical linear regression, expectile regression (ER) explores the relationship between the conditional expectile of a response variable and a set of predictor variables. ER with respect to different expectile levels can provide a comprehensive picture of the conditional distribution of the response variable given the predictors. We adopt an efficient estimation method called the envelope model ([8]) in ER, and construct a novel envelope expectile regression (EER) model. Estimation of the EER parameters can be performed using the generalized method of moments (GMM). We establish the consistency and derive the asymptotic distribution of the EER estimators. In addition, we show that the EER estimators are asymptotically more efficient than the ER estimators. Numerical experiments and real data examples are provided to demonstrate the efficiency gains attained by EER compared to ER, and the efficiency gains can further lead to improvements in prediction.

A common divide-and-conquer approach for Bayesian computation with big data is to partition the data, perform local inference for each piece separately, and combine the results to obtain a global posterior approximation. While being conceptually and computationally appealing, this method involves the problematic need to also split the prior for the local inferences; these weakened priors may not provide enough regularization for each separate computation, thus eliminating one of the key advantages of Bayesian methods. To resolve this dilemma while still retaining the generalizability of the underlying local inference method, we apply the idea of expectation propagation (EP) as a framework for distributed Bayesian inference. The central idea is to iteratively update approximations to the local likelihoods given the state of the other approximations and the prior. The present paper has two roles: we review the steps that are needed to keep EP algorithms numerically stable, and we suggest a general approach, inspired by EP, for approaching data partitioning problems in a way that achieves the computational benefits of parallelism while allowing each local update to make use of relevant information from the other sites. In addition, we demonstrate how the method can be applied in a hierarchical context to make use of partitioning of both data and parameters. The paper describes a general algorithmic framework, rather than a specific algorithm, and presents an example implementation for it.

We propose expected policy gradients (EPG), which unify stochastic policy gradients (SPG) and deterministic policy gradients (DPG) for reinforcement learning. Inspired by expected sarsa, EPG integrates (or sums) across actions when estimating the gradient, instead of relying only on the action in the sampled trajectory. For continuous action spaces, we first derive a practical result for Gaussian policies and quadratic critics and then extend it to a universal analytical method, covering a broad class of actors and critics, including Gaussian, exponential families, and policies with bounded support. For Gaussian policies, we introduce an exploration method that uses covariance proportional to the matrix exponential of the scaled Hessian of the critic with respect to the actions. For discrete action spaces, we derive a variant of EPG based on softmax policies. We also establish a new general policy gradient theorem, of which the stochastic and deterministic policy gradient theorems are special cases. Furthermore, we prove that EPG reduces the variance of the gradient estimates without requiring deterministic policies and with little computational overhead. Finally, we provide an extensive experimental evaluation of EPG and show that it outperforms existing approaches on multiple challenging control domains.

Sylvia Frühwirth-Schnatter.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 4, 706--733.

Abstract:
Finite mixture models and their extensions to Markov mixture and mixture of experts models are very popular in analysing data of various kind. A challenge for these models is choosing the number of components based on marginal likelihoods. The present paper suggests two innovative, generic bridge sampling estimators of the marginal likelihood that are based on constructing balanced importance densities from the conditional densities arising during Gibbs sampling. The full permutation bridge sampling estimator is derived from considering all possible permutations of the mixture labels for a subset of these densities. For the double random permutation bridge sampling estimator, two levels of random permutations are applied, first to permute the labels of the MCMC draws and second to randomly permute the labels of the conditional densities arising during Gibbs sampling. Various applications show very good performance of these estimators in comparison to importance and to reciprocal importance sampling estimators derived from the same importance densities.

Beginning from a basic neural-network architecture, we test the potential benefits offered by a range of advanced techniques for machine learning, in particular deep learning, in the context of a typical classification problem encountered in the domain of high-energy physics, using a well-studied dataset: the 2014 Higgs ML Kaggle dataset. The advantages are evaluated in terms of both performance metrics and the time required to train and apply the resulting models. Techniques examined include domain-specific data-augmentation, learning rate and momentum scheduling, (advanced) ensembling in both model-space and weight-space, and alternative architectures and connection methods.

Following the investigation, we arrive at a model which achieves equal performance to the winning solution of the original Kaggle challenge, whilst being significantly quicker to train and apply, and being suitable for use with both GPU and CPU hardware setups. These reductions in timing and hardware requirements potentially allow the use of more powerful algorithms in HEP analyses, where models must be retrained frequently, sometimes at short notice, by small groups of researchers with limited hardware resources. Additionally, a new wrapper library for PyTorch called LUMINis presented, which incorporates all of the techniques studied.

I study the minimax-optimal design for a two-arm controlled experiment where conditional mean outcomes may vary in a given set. When this set is permutation symmetric, the optimal design is complete randomization, and using a single partition (i.e., the design that only randomizes the treatment labels for each side of the partition) has minimax risk larger by a factor of $n-1$. More generally, the optimal design is shown to be the mixed-strategy optimal design (MSOD) of Kallus (2018). Notably, even when the set of conditional mean outcomes has structure (i.e., is not permutation symmetric), being minimax-optimal for variance still requires randomization beyond a single partition. Nonetheless, since this targets precision, it may still not ensure sufficient uniformity in randomization to enable randomization (i.e., design-based) inference by Fisher's exact test to appropriately detect violations of null. I therefore propose the inference-constrained MSOD, which is minimax-optimal among all designs subject to such uniformity constraints. On the way, I discuss Johansson et al. (2020) who recently compared rerandomization of Morgan and Rubin (2012) and the pure-strategy optimal design (PSOD) of Kallus (2018). I point out some errors therein and set straight that randomization is minimax-optimal and that the "no free lunch" theorem and example in Kallus (2018) are correct.

Xinran Li, Peng Ding, Donald B. Rubin.

Source: The Annals of Statistics, Volume 48, Number 1, 43--63.

Abstract:
With many pretreatment covariates and treatment factors, the classical factorial experiment often fails to balance covariates across multiple factorial effects simultaneously. Therefore, it is intuitive to restrict the randomization of the treatment factors to satisfy certain covariate balance criteria, possibly conforming to the tiers of factorial effects and covariates based on their relative importances. This is rerandomization in factorial experiments. We study the asymptotic properties of this experimental design under the randomization inference framework without imposing any distributional or modeling assumptions of the covariates and outcomes. We derive the joint asymptotic sampling distribution of the usual estimators of the factorial effects, and show that it is symmetric, unimodal and more “concentrated” at the true factorial effects under rerandomization than under the classical factorial experiment. We quantify this advantage of rerandomization using the notions of “central convex unimodality” and “peakedness” of the joint asymptotic sampling distribution. We also construct conservative large-sample confidence sets for the factorial effects.

Kun Liang.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2452--2482.

Abstract:
Finding differentially expressed genes is a common task in high-throughput transcriptome studies. While traditional statistical methods rank the genes by their test statistics alone, we analyze an RNA sequencing dataset using the auxiliary information of gene length and the test statistics from a related microarray study. Given the auxiliary information, we propose a novel nonparametric empirical Bayes procedure to estimate the posterior probability of differential expression for each gene. We demonstrate the advantage of our procedure in extensive simulation studies and a psoriasis RNA sequencing study. The companion R package calm is available at Bioconductor.

Abdelaati Daouia, Stéphane Girard, Gilles Stupfler.

Source: Bernoulli, Volume 26, Number 1, 531--556.

Abstract:
Expectiles define a least squares analogue of quantiles. They are determined by tail expectations rather than tail probabilities. For this reason and many other theoretical and practical merits, expectiles have recently received a lot of attention, especially in actuarial and financial risk management. Their estimation, however, typically requires to consider non-explicit asymmetric least squares estimates rather than the traditional order statistics used for quantile estimation. This makes the study of the tail expectile process a lot harder than that of the standard tail quantile process. Under the challenging model of heavy-tailed distributions, we derive joint weighted Gaussian approximations of the tail empirical expectile and quantile processes. We then use this powerful result to introduce and study new estimators of extreme expectiles and the standard quantile-based expected shortfall, as well as a novel expectile-based form of expected shortfall. Our estimators are built on general weighted combinations of both top order statistics and asymmetric least squares estimates. Some numerical simulations and applications to actuarial and financial data are provided.

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.

Members of the future American workforce could see losses of earnings that add up to trillions of dollars, depending on how long coronavirus-related school closures persist.

The post Economists Expect Huge Future Earnings Loss for Students Missing School Due to COVID-19 appeared first on Market Brief.

The New York City police department (NYPD) is conducting an internal investigation into a May 2 incident involving the violent arrests of multiple people, allegedly members of a group who were not social distancing

Some Buckeyes are not comfortable being told by a "woman in power" to quarantine, one expert said.