Chao Du, S. C. Kou.

Source: Statistical Science, Volume 35, Number 1, 75--91.

Abstract:
Toward the last quarter of the 20th century, the emergence of single-molecule experiments enabled scientists to track and study individual molecules’ dynamic properties in real time. Unlike macroscopic systems’ dynamics, those of single molecules can only be properly described by stochastic models even in the absence of external noise. Consequently, statistical methods have played a key role in extracting hidden information about molecular dynamics from data obtained through single-molecule experiments. In this article, we survey the major statistical methodologies used to analyze single-molecule experimental data. Our discussion is organized according to the types of stochastic models used to describe single-molecule systems as well as major experimental data collection techniques. We also highlight challenges and future directions in the application of statistical methodologies to single-molecule experiments.

Jacqueline J. Meulman, Anita J. van der Kooij, Kevin L. W. Duisters.

Source: Statistical Science, Volume 34, Number 3, 361--390.

Abstract:
We present a methodology for multiple regression analysis that deals with categorical variables (possibly mixed with continuous ones), in combination with regularization, variable selection and high-dimensional data ($Pgg N$). Regularization and optimal scaling (OS) are two important extensions of ordinary least squares regression (OLS) that will be combined in this paper. There are two data analytic situations for which optimal scaling was developed. One is the analysis of categorical data, and the other the need for transformations because of nonlinear relationships between predictors and outcome. Optimal scaling of categorical data finds quantifications for the categories, both for the predictors and for the outcome variables, that are optimal for the regression model in the sense that they maximize the multiple correlation. When nonlinear relationships exist, nonlinear transformation of predictors and outcome maximize the multiple correlation in the same way. We will consider a variety of transformation types; typically we use step functions for categorical variables, and smooth (spline) functions for continuous variables. Both types of functions can be restricted to be monotonic, preserving the ordinal information in the data. In combination with optimal scaling, three popular regularization methods will be considered: Ridge regression, the Lasso and the Elastic Net. The resulting method will be called ROS Regression (Regularized Optimal Scaling Regression). The OS algorithm provides straightforward and efficient estimation of the regularized regression coefficients, automatically gives the Group Lasso and Blockwise Sparse Regression, and extends them by the possibility to maintain ordinal properties in the data. Extended examples are provided.

Nathan B. Cruze, Andreea L. Erciulescu, Balgobin Nandram, Wendy J. Barboza, Linda J. Young.

Source: Statistical Science, Volume 34, Number 2, 301--316.

Abstract:
In the United States, county-level estimates of crop yield, production, and acreage published by the United States Department of Agriculture’s National Agricultural Statistics Service (USDA NASS) play an important role in determining the value of payments allotted to farmers and ranchers enrolled in several federal programs. Given the importance of these official county-level crop estimates, NASS continually strives to improve its crops county estimates program in terms of accuracy, reliability and coverage. In 2015, NASS engaged a panel of experts convened under the auspices of the National Academies of Sciences, Engineering, and Medicine Committee on National Statistics (CNSTAT) for guidance on implementing models that may synthesize multiple sources of information into a single estimate, provide defensible measures of uncertainty, and potentially increase the number of publishable county estimates. The final report titled Improving Crop Estimates by Integrating Multiple Data Sources was released in 2017. This paper discusses several needs and requirements for NASS county-level crop estimates that were illuminated during the activities of the CNSTAT panel. A motivating example of planted acreage estimation in Illinois illustrates several challenges that NASS faces as it considers adopting any explicit model for official crops county estimates.

Víctor Elvira, Luca Martino, David Luengo, Mónica F. Bugallo.

Source: Statistical Science, Volume 34, Number 1, 129--155.

Abstract:
Importance sampling (IS) methods are broadly used to approximate posterior distributions or their moments. In the standard IS approach, samples are drawn from a single proposal distribution and weighted adequately. However, since the performance in IS depends on the mismatch between the targeted and the proposal distributions, several proposal densities are often employed for the generation of samples. Under this multiple importance sampling (MIS) scenario, extensive literature has addressed the selection and adaptation of the proposal distributions, interpreting the sampling and weighting steps in different ways. In this paper, we establish a novel general framework with sampling and weighting procedures when more than one proposal is available. The new framework encompasses most relevant MIS schemes in the literature, and novel valid schemes appear naturally. All the MIS schemes are compared and ranked in terms of the variance of the associated estimators. Finally, we provide illustrative examples revealing that, even with a good choice of the proposal densities, a careful interpretation of the sampling and weighting procedures can make a significant difference in the performance of the method.

Ehud Karavani, Tal El-Hay, Yishai Shimoni, Chen Yanover.

Source: Statistical Science, Volume 34, Number 1, 86--89.

Abstract:
Data competitions proved to be highly beneficial to the field of machine learning, and thus expected to provide similar advantages in the field of causal inference. As participants in the 2016 and 2017 Atlantic Causal Inference Conference (ACIC) data competitions and co-organizers of the 2018 competition, we discuss the strengths of simulation-based competitions and suggest potential extensions to address their limitations. These suggested augmentations aim at making the data generating processes more realistic and gradually increase in complexity, allowing thorough investigations of algorithms’ performance. We further outline a community-wide competition framework to evaluate an end-to-end causal inference pipeline, beginning with a causal question and a database, and ending with causal estimates.

London (33 Stillness Road, London SE23 1NG) : Cleanair, Campaign for a Smoke-free Environment, [1989?]

London (33 Stillness Rd, London, SE23 1NG) : Cleanair, Campaign for a Smoke-free Environment, [198-?]

London (33 Stllness Rd, London, SE23 1NG) : Cleanair, Campaign for a Smoke-free Environment, [198-?]

London (33 Stillness Rd, London, SE23 ING) : Cleanair, Campaign for a Smoke-free Environment, [198-?]

London (33 Stillness Rd, London, SE23 1NG) : Cleanair, Campaign for a Smoke-free Environment, [198-?]

London (33 Stillness Rd, London, SE23 1NG) : Cleanair, Campaign for a Smoke-free Environment, [198-?]

London : Cleanair, Smoke-free Environment (33 Stillness Rd, London, SE23 1NG), [198-?]

London : Cleanair, Smoke-free Environment (33 Stillness Rd, London, SE23 1NG), [198-?]

London : Cleanair, Smoke-free Environment (33 Stillness Rd, London, SE23 1NG), [198-?]

London : Cleanair, Smoke-free Environment (33 Stillness Rd, London, SE23 1NG), [198-?]

London : Cleanair, Smoke-free Environment (33 Stillness Rd, London, SE23 1NG), [198-?]

London : Cleanair, Smoke-free Environment (33 Stillness Rd, London, SE23 1NG), [198-?]

London : Cleanair, Smoke-free Environment (33 Stillness Rd, London, SE23 1NG), [198-?]

London : Cleanair, Smoke-free Environment (33 Stillness Rd, London, SE23 1NG), [198-?]

London (33 Stillness Rd, SE23 1NG) : Cleanair, Campaign for a Smoke-free Environment, [198-?]

London : Cleanair, Smoke-free Environment (33 Stillness Rd, London, SE23 1NG), [198-?]

London (33 Stillness Rd, London, SE23 1NG) : Cleanair, Campaign for a Smoke-free Environment, [198-?]

London : Cleanair, Smoke-free Environment (33 Stillness Rd, London, SE23 1NG), [198-?]

London : Cleanair, [1988?]

London : Cleanair, [1988?]

London : Cleanair, [1988?]

London : Cleanair, [1988?]

London (33 Stillness Road, London SE23 1NG) : Cleanair, [1989?]

London (33 Stillness Rd, London, SE23 1NG) : Cleanair, Campaign for a Smoke-free Environment, [198-?]

London : Cleanair, Smoke-free Environment (33 Stillness Rd, London, SE23 1NG), [198-?]

London : Cleanair, Smoke-free Environment (33 Stillness Rd, London, SE23 1NG), [198-?]

London : Cleanair (33 Stillness Rd, London, SE23 1NG), [1989?]

London : Cleanair (33 Stillness Rd, London, SE23 1NG), [198-?]

London : Cleanair (33 Stillness Rd, London, SE23 1NG), [198-?]

London : Cleanair (33 Stillness Rd, London, SE23 1NG), [198-?]

London : Cleanair (33 Stillness Rd, London, SE23 1NG), [198-?]

London : Cleanair (33 Stillness Rd, London, SE23 1NG), [198-?]

[London] : Cleanair, Campaña para un Medio Ambiente Libre de Humo, [198-?]

[London] : Cleanair, Campaña para un Medio Ambiente Libre de Humo, [198-?]

[London] : Cleanair, [1989?]

London (33 Stillness Road London SE23 1NG) : Cleanair Campaign for a Smoke-free Environment, [198-?]

London : Cleanair Campaña para un Medio Ambiente Libre de Humo, [198-?]

London (33 Stillness Rd, SE23 1NG) : Cleanair, [198-?]

London (33 Stillness Road, London SE23 ING) : Cleanair, [198-?]

[London?], [199-?]

[London?], [6th June 1990]

[London?], [199-?]

[London?], 6th November 1989.

[London?], [199-?]

London, 26 January 2003.