Unit-root tests for explosive behavior

We present a new command, radf, that tests for explosive behavior in time series. The command computes the right-tail augmented Dickey and Fuller (1979, Journal of the American Statistical Association 74: 427–431) unitroot test and its further developments based on supremum statistics derived from augmented Dickey–Fuller-type regressions estimated using recursive windows (Phillips, Wu, and Yu, 2011, International Economic Review 52: 201–226) and recursive flexible windows (Phillips, Shi, and Yu, 2015, International Economic Review 56: 1043–1078). It allows for the lag length in the test regression and the width of rolling windows to be either specified by the user or determined using data-dependent procedures, and it performs the date-stamping procedures advocated by Phillips, Wu, and Yu (2011) and Phillips, Shi, and Yu (2015) to identify episodes of explosive behavior. It also implements the wild bootstrap proposed by Phillips and Shi (2020, Handbook of Statistics: Financial, Macro and Micro Econometrics Using R, Vol. 42, 61–80) to lessen the potential effects of unconditional heteroskedasticity and account for the multiplicity issue in recursive testing. The use of radf is illustrated with an empirical example.

A New Paradigm for Safety Data Signal Detection and Evaluation Using Open-Source Software Created by an Interdisciplinary Working Group

Techniques to evaluate large amounts of safety data continue to evolve based on a greater understanding of how the brain processes visual information and the advancement of programing tools. The Interactive Safety Graphics Task Force of the American Statistical Association Biopharmaceutical Safety Working Group has assembled a multidisciplinary team of experts in a variety of domains to develop the next generation of open-source visual analytical tools for safety data based on these advances. The multidisciplinary approach resulted in the rapid development of the first tool, a novel interactive version of the familiar Evaluation of Drug-Induced Serious Hepatotoxicity (eDISH) graphic along with a unique clinical workflow to guide the reviewer through the data analysis. This now serves as the model for the team to expand the open-source platform into a suite of other interactive safety analysis tools.

Implementation of geostatistical models for large spatiotemporal datasets using multi-resolution approximations

<p>The multi-resolution approximation approach (MRA) [1] provides an efficient representation of Gaussian processes that scales beyond millions of observations. MRA leaves flexibility in the selection of covariance functions and allows to trade off computation time against prediction performance, depending on the selection of parameters. Recent work [2] has shown how MRA can be used for global spatiotemporal processes by integrating nonstationary covariance functions, where parameters vary over space and/or time following a kernel convolution approach. As such, MRA turns out to be a promising approach for geostatistical modelling of global spatiotemporal datasets, such as those coming from Earth observation satellites.</p><p>In this work, we show how MRA can be used for spatiotemporal analysis from a practical perspective. In the first part, we will discuss the influence of parameters (spatiotemporal shape of partitioning regions, the number of basis functions, and the number of partitioning levels) by analyzing a real world dataset. In the second part, we will present and discuss our implementation as an R package stmra[3]. We will demonstrate how traditional models as from the gstat package can be implemented efficiently with MRA, and how non-stationary models can be defined by users in a relatively simple way. </p><p>[1] Katzfuss, M. (2017). A multi-resolution approximation for massive spatial datasets. Journal of the American Statistical Association, 112(517), 201-214</p><p>[2] Appel, M., & Pebesma, E. (2020). Spatiotemporal multi-resolution approximations for analyzing global environmental data. Spatial Statistics, 38, 100465.</p><p>[3] https://github.com/appelmar/stmra</p>

بحوث العلوم النفسية: الاستدلال الإحصائي والأزمة في قابلية التكرار

تتراكم المعرفة نتيجة لتتابع البحوث وتطورها، ويعتبر البحث العلمي أداة ووسيلة موضوعية للكشف عن الحقيقة العلمية، والعلم يقوم بالتصحيح الذاتي ليصحح مساره. تواجه بحوث العلوم النفسية قضايا منهجية مرتبطة بالاستدلال الإحصائي وبالتحديد سوء فهم واستخدام الدلالة الإحصائية أو القيم الاحتمالية (p-values)، وعدم قابلية النتائج للتكـرار. يقوم الباحثون بعملية تجريف البيانات (Data-dredging) لإيجاد نتائج ذات دلالة إحصائية تبرر النشر وذلك نتيجة للتنافس العالي في البيئة الأكاديميـة. تقدم هذه الورقة جهود العلماء والباحثين في علم النفس والإحصاءتجاه هذه القضايا، بما فيها بيان أصدرته الجمعية الإحصائية الأمريكية (ASA) American Statistical Association عن الدلالة الإحصائية والقيم الاحتمالية، والإطار العلمي المفتوح (OSF) Open Science Framework، ومشروع تعاوني قام به التعاون العلمي المفتوح بحيث تم تكرار 100 دراسة تجريبية وارتباطية للحصول على تقدير مبدئي في قابلية نتائج البحوث النفسية للتكرار. كما تقدم الورقة عدد من الحلول المقترحـة. يؤمل أن تحفـز هذه الورقة النقاش في وسط الباحثين وأن تفتح أفق جديدة للبحوث النفسيــة.

Spatial survival modelling of business re-opening after Katrina: Survival modelling compared to spatial probit modelling of re-opening within 3, 6 or 12 months

Zhou and Hanson; Zhou and Hanson; Zhou and Hanson ( 2015 , Nonparametric Bayesian Inference in Biostatistics, pages 215–46. Cham: Springer; 2018, Journal of the American Statistical Association, 113, 571–81; 2020, spBayesSurv: Bayesian Modeling and Analysis of Spatially Correlated Survival Data. R package version 1.1.4) and Zhou et al. (2020, Journal of Statistical Software, Articles, 92, 1–33) present methods for estimating spatial survival models using areal data. This article applies their methods to a dataset recording New Orleans business decisions to re-open after Hurricane Katrina; the data were included in LeSage et al. (2011b , Journal of the Royal Statistical Society: Series A (Statistics in Society), 174, 1007—27). In two articles ( LeSage etal., 2011a , Significance, 8, 160—63; 2011b, Journal of the Royal Statistical Society: Series A (Statistics in Society), 174, 1007—27), spatial probit models are used to model spatial dependence in this dataset, with decisions to re-open aggregated to the first 90, 180 and 360 days. We re-cast the problem as one of examining the time-to-event records in the data, right-censored as observations ceased before 175 businesses had re-opened; we omit businesses already re-opened when observations began on Day 41. We are interested in checking whether the conclusions about the covariates using aspatial and spatial probit models are modified when applying survival and spatial survival models estimated using MCMC and INLA. In general, we find that the same covariates are associated with re-opening decisions in both modelling approaches. We do however find that data collected from three streets differ substantially, and that the streets are probably better handled separately or that the street effect should be included explicitly.

Constructing a summary index using the standardized inverse-covariance weighted average of indicators

Researchers often want to examine the relationship between a variable of interest and multiple related outcomes. To avoid problems of inference that arise from testing multiple hypotheses, one can create a summary index of the outcomes. Summary indices facilitate generalizing findings and can be more powerful than individual tests. In this article, we introduce a command, swindex, that implements the generalized least-squares method of index construction proposed by Anderson (2008, Journal of the American Statistical Association 103: 1481–1495). We describe the command and its options and provide an example based on Blattman, Fiala, and Martinez’s (2014, Quarterly Journal of Economics 129: 697–752) evaluation of a cash transfer program in Uganda.

Nonparametric synthetic control using the npsynth command

In this article, I build on the work of Abadie and Gardeazabal (2003, American Economic Review 93: 113–132) and Abadie, Diamond, and Hainmueller (2010, Journal of the American Statistical Association 105: 493–505), extending the synthetic control method for program evaluation—implemented in Stata via the community-contributed command synth—to the case of a nonparametric identification of the synthetic (or counterfactual) time pattern of a treated unit (a country, a region, a city, etc.) subject to a specific intervention in a given time. After theoretical description of the model, I present npsynth, the command I developed for estimating the nonparametric synthetic control method proposed in this article. Using both simulated and real data, I set out a comparison of the performance of the parametric and nonparametric methods and widely discuss the results.