U.S. flag

An official website of the United States government

Dot gov

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Https

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

PubAg

Main content area

You searched for: Journal name Journal of the American Statistical Association Remove constraint Journal name: Journal of the American Statistical Association Publication year rev 7978-2022 Remove constraint Publication year rev: 7978-2022 Source 2022 v.117 no.537 Remove constraint Source: 2022 v.117 no.537
Start Over

Search

35  Search Results

« Previous | 1 - 20 of 35 |
Number of results to display per page

Search Results

1. A Multiple-Testing Procedure for High-Dimensional Mediation Hypotheses

Author:
James Y. Dai; Janet L. Stanford; Michael LeBlanc
Source:
Journal of the American Statistical Association 2022 v.117 no.537 pp. 198-213
ISSN:
1537-274X
Subject:
DNA methylation; exercise; gene expression regulation; neoplasm progression; prostatic neoplasms
Abstract:
... Mediation analysis is of rising interest in epidemiologic studies and clinical trials. Among existing methods, the joint significance test yields an overly conservative Type I error rate and low power, particularly for high-dimensional mediation hypotheses. In this article, we develop a multiple-testing procedure that accurately controls the family-wise error rate (FWER) and the false discovery ra ...
DOI:
10.1080/01621459.2020.1765785

2. Covariate Adaptive False Discovery Rate Control With Applications to Omics-Wide Multiple Testing

Author:
Xianyang Zhang; Jun Chen
Source:
Journal of the American Statistical Association 2022 v.117 no.537 pp. 411-427
ISSN:
1537-274X
Subject:
algorithms; data collection; genomics; models
Abstract:
... Conventional multiple testing procedures often assume hypotheses for different features are exchangeable. However, in many scientific applications, additional covariate information regarding the patterns of signals and nulls are available. In this article, we introduce an FDR control procedure in large-scale inference problem that can incorporate covariate information. We develop a fast algorithm ...
DOI:
10.1080/01621459.2020.1783273

3. Detection of Local Differences in Spatial Characteristics Between Two Spatiotemporal Random Fields

Author:
Sooin Yun; Xianyang Zhang; Bo Li
Source:
Journal of the American Statistical Association 2022 v.117 no.537 pp. 291-306
ISSN:
1537-274X
Subject:
climate; models; probability; spatial data; spatial variation
Abstract:
... Comparing the spatial characteristics of spatiotemporal random fields is often at demand. However, the comparison can be challenging due to the high-dimensional feature and dependency in the data. We develop a new multiple testing approach to detect local differences in the spatial characteristics of two spatiotemporal random fields by taking the spatial information into account. Our method adopts ...
DOI:
10.1080/01621459.2020.1775613

4. Earthquake Risk Embedded in Property Prices: Evidence From Five Japanese Cities

Author:
Masako Ikefuji; Roger J. A. Laeven; Jan R. Magnus; Yuan Yue
Source:
Journal of the American Statistical Association 2022 v.117 no.537 pp. 82-93
ISSN:
1537-274X
Subject:
data collection; earthquakes; geophysics; income; macroeconomics; models; prices; risk; statistical analysis; variance
Abstract:
... We analyze the impact of short-run (90 days) and long-run (30 years) earthquake risk on real estate transaction prices in five Japanese cities (Tokyo, Osaka, Nagoya, Fukuoka, and Sapporo), using quarterly data over the period 2006–2015. We exploit a rich panel dataset (331,343 observations) with property characteristics, ward attractiveness information, macroeconomic variables, and long-run seismi ...
DOI:
10.1080/01621459.2021.1928512

5. Embedding Learning

Author:
Ben Dai; Xiaotong Shen; Junhui Wang
Source:
Journal of the American Statistical Association 2022 v.117 no.537 pp. 307-319
ISSN:
1537-274X
Subject:
Americans; algorithms; data analysis; dimensions; graphs; journals; learning; neural networks; regression analysis
Abstract:
... Numerical embedding has become one standard technique for processing and analyzing unstructured data that cannot be expressed in a predefined fashion. It stores the main characteristics of data by mapping it onto a numerical vector. An embedding is often unsupervised and constructed by transfer learning from large-scale unannotated data. Given an embedding, a downstream learning method, referred t ...
DOI:
10.1080/01621459.2020.1775614

6. Estimating a Change Point in a Sequence of Very High-Dimensional Covariance Matrices

Author:
Holger Dette; Guangming Pan; Qing Yang
Source:
Journal of the American Statistical Association 2022 v.117 no.537 pp. 444-454
ISSN:
1537-274X
Subject:
Americans; dimensions; journals; sample size; variance covariance matrix
Abstract:
... This article considers the problem of estimating a change point in the covariance matrix in a sequence of high-dimensional vectors, where the dimension is substantially larger than the sample size. A two-stage approach is proposed to efficiently estimate the location of the change point. The first step consists of a reduction of the dimension to identify elements of the covariance matrices corresp ...
DOI:
10.1080/01621459.2020.1785477

7. Estimation of Subgraph Densities in Noisy Networks

Author:
Jinyuan Chang; Eric D. Kolaczyk; Qiwei Yao
Source:
Journal of the American Statistical Association 2022 v.117 no.537 pp. 361-374
ISSN:
1537-274X
Subject:
genes; models; uncertainty
Abstract:
... While it is common practice in applied network analysis to report various standard network summary statistics, these numbers are rarely accompanied by uncertainty quantification. Yet any error inherent in the measurements underlying the construction of the network, or in the network construction procedure itself, necessarily must propagate to any summary statistics reported. Here we study the prob ...
DOI:
10.1080/01621459.2020.1778482

8. Irrational Exuberance: Correcting Bias in Probability Estimates

Author:
Gareth M. James; Peter Radchenko; Bradley Rava
Source:
Journal of the American Statistical Association 2022 v.117 no.537 pp. 455-468
ISSN:
1537-274X
Subject:
Americans; bias; data collection; journals; risk; uncertainty
Abstract:
... We consider the common setting where one observes probability estimates for a large number of events, such as default risks for numerous bonds. Unfortunately, even with unbiased estimates, selecting events corresponding to the most extreme probabilities can result in systematically underestimating the true level of uncertainty. We develop an empirical Bayes approach “excess certainty adjusted prob ...
DOI:
10.1080/01621459.2020.1787175

9. Joint Structural Break Detection and Parameter Estimation in High-Dimensional Nonstationary VAR Models

Author:
Abolfazl Safikhani; Ali Shojaie
Source:
Journal of the American Statistical Association 2022 v.117 no.537 pp. 251-264
ISSN:
1537-274X
Subject:
Americans; application timing; journals; least squares; methodology; models; selection criteria; time series analysis
Abstract:
... Assuming stationarity is unrealistic in many time series applications. A more realistic alternative is to assume piecewise stationarity, where the model can change at potentially many change points. We propose a three-stage procedure for simultaneous estimation of change points and parameters of high-dimensional piecewise vector autoregressive (VAR) models. In the first step, we reformulate the ch ...
DOI:
10.1080/01621459.2020.1770097

10. Large-Scale Hypothesis Testing for Causal Mediation Effects with Applications in Genome-wide Epigenetic Studies

Author:
Zhonghua Liu; Jincheng Shen; Richard Barfield; Joel Schwartz; Andrea A. Baccarelli; Xihong Lin
Source:
Journal of the American Statistical Association 2022 v.117 no.537 pp. 67-81
ISSN:
1537-274X
Subject:
DNA; DNA methylation; cohort studies; epigenetics; epigenome; lung function; normal distribution; statistical inference
Abstract:
... In genome-wide epigenetic studies, it is of great scientific interest to assess whether the effect of an exposure on a clinical outcome is mediated through DNA methylations. However, statistical inference for causal mediation effects is challenged by the fact that one needs to test a large number of composite null hypotheses across the whole epigenome. Two popular tests, the Wald-type Sobel’s test ...
DOI:
10.1080/01621459.2021.1914634

11. Mean and Covariance Estimation for Functional Snippets

Author:
Zhenhua Lin; Jane-Ling Wang
Source:
Journal of the American Statistical Association 2022 v.117 no.537 pp. 348-360
ISSN:
1537-274X
Subject:
Americans; covariance; journals; measurement; simulation models; variance
Abstract:
... We consider estimation of mean and covariance functions of functional snippets, which are short segments of functions possibly observed irregularly on an individual specific subinterval that is much shorter than the entire study interval. Estimation of the covariance function for functional snippets is challenging since information for the far off-diagonal regions of the covariance structure is co ...
DOI:
10.1080/01621459.2020.1777138

12. Mini-Batch Metropolis–Hastings With Reversible SGLD Proposal

Author:
Tung-Yu Wu; Y. X. Rachel Wang; Wing H. Wong
Source:
Journal of the American Statistical Association 2022 v.117 no.537 pp. 386-394
ISSN:
1537-274X
Subject:
Markov chain; algorithms; data collection
Abstract:
... Traditional Markov chain Monte Carlo (MCMC) algorithms are computationally intensive and do not scale well to large data. In particular, the Metropolis–Hastings (MH) algorithm requires passing over the entire dataset to evaluate the likelihood ratio in each iteration. We propose a general framework for performing MH-MCMC using mini-batches of the whole dataset and show that this gives rise to appr ...
DOI:
10.1080/01621459.2020.1782222

13. Model-Free Feature Screening and FDR Control With Knockoff Features

Author:
Wanjun Liu; Yuan Ke; Jingyuan Liu; Runze Li
Source:
Journal of the American Statistical Association 2022 v.117 no.537 pp. 428-443
ISSN:
1537-274X
Subject:
Americans; journals; regression analysis; screening
Abstract:
... This article proposes a model-free and data-adaptive feature screening method for ultrahigh-dimensional data. The proposed method is based on the projection correlation which measures the dependence between two random vectors. This projection correlation based method does not require specifying a regression model, and applies to data in the presence of heavy tails and multivariate responses. It en ...
DOI:
10.1080/01621459.2020.1783274

14. On Design Orthogonality, Maximin Distance, and Projection Uniformity for Computer Experiments

Author:
Yaping Wang; Fasheng Sun; Hongquan Xu
Source:
Journal of the American Statistical Association 2022 v.117 no.537 pp. 375-385
ISSN:
1537-274X
Subject:
Americans; clothing; computers; journals
Abstract:
... Space-filling designs are widely used in both computer and physical experiments. Column-orthogonality, maximin distance, and projection uniformity are three basic and popular space-filling criteria proposed from different perspectives, but their relationships have been rarely investigated. We show that the average squared correlation metric is a function of the pairwise L ₂-distances between the r ...
DOI:
10.1080/01621459.2020.1782221

15. Separable Effects for Causal Inference in the Presence of Competing Events

Author:
Mats J. Stensrud; Jessica G. Young; Vanessa Didelez; James M. Robins; Miguel A. Hernán
Source:
Journal of the American Statistical Association 2022 v.117 no.537 pp. 175-183
ISSN:
1537-274X
Subject:
death; estrogens; prostatic neoplasms; randomized clinical trials; therapeutics
Abstract:
... In time-to-event settings, the presence of competing events complicates the definition of causal effects. Here we propose the new separable effects to study the causal effect of a treatment on an event of interest. The separable direct effect is the treatment effect on the event of interest not mediated by its effect on the competing event. The separable indirect effect is the treatment effect on ...
DOI:
10.1080/01621459.2020.1765783

16. “Generalized Measures of Correlation for Asymmetry, Nonlinearity, and Beyond”: Some Antecedents on Causality

Author:
David E. Allen; Michael McAleer
Source:
Journal of the American Statistical Association 2022 v.117 no.537 pp. 214-224
ISSN:
1537-274X
Subject:
Americans; asymmetry; journals
Abstract:
... This note comments on the generalized measure of correlation (GMC) that was suggested by Zheng, Shi, and Zhang. The GMC concept was partly anticipated in some publications over 100 years earlier by Yule in the Proceedings of the Royal Society, and by Kendall. Other antecedents discussed include work on dependency by Renyi and Doksum and Samarov, together with the Yule–Simpson paradox. The GMC metr ...
DOI:
10.1080/01621459.2020.1768101

17. AdaBoost Semiparametric Model Averaging Prediction for Multiple Categories

Author:
Jialiang Li; Jing Lv; Alan T. K. Wan; Jun Liao
Source:
Journal of the American Statistical Association 2022 v.117 no.537 pp. 495-509
ISSN:
1537-274X
Subject:
algorithms; automobiles; models; prediction
Abstract:
... Model average techniques are very useful for model-based prediction. However, most earlier works in this field focused on parametric models and continuous responses. In this article, we study varying coefficient multinomial logistic models and propose a semiparametric model averaging prediction (SMAP) approach for multi-category outcomes. The proposed procedure does not need any artificial specifi ...
DOI:
10.1080/01621459.2020.1790375

18. An Empirical Bayes Method for Chi-Squared Data

Author:
Lilun Du; Inchi Hu
Source:
Journal of the American Statistical Association 2022 v.117 no.537 pp. 334-347
ISSN:
1537-274X
Subject:
Americans; Bayesian theory; data analysis; empirical research; journals; logging; normal distribution; selection methods
Abstract:
... In a thought-provoking paper, Efron investigated the merit and limitation of an empirical Bayes method to correct selection bias based on Tweedie’s formula first reported in the study by Robbins. The exceptional virtue of Tweedie’s formula for the normal distribution lies in its representation of selection bias as a simple function of the derivative of log marginal likelihood. Since the marginal l ...
DOI:
10.1080/01621459.2020.1777137

19. Distribution-Free Consistent Independence Tests via Center-Outward Ranks and Signs

Author:
Hongjian Shi; Mathias Drton; Fang Han
Source:
Journal of the American Statistical Association 2022 v.117 no.537 pp. 395-410
ISSN:
1537-274X
Subject:
Americans; covariance; journals; probability distribution; sample size; testing
Abstract:
... This article investigates the problem of testing independence of two random vectors of general dimensions. For this, we give for the first time a distribution-free consistent test. Our approach combines distance covariance with the center-outward ranks and signs developed by Marc Hallin and collaborators. In technical terms, the proposed test is consistent and distribution-free in the family of mu ...
DOI:
10.1080/01621459.2020.1782223

20. Estimating and Accounting for Unobserved Covariates in High-Dimensional Correlated Data

Author:
Chris McKennan; Dan Nicolae
Source:
Journal of the American Statistical Association 2022 v.117 no.537 pp. 225-236
ISSN:
1537-274X
Subject:
DNA methylation; data collection; gene expression; researchers
Abstract:
... Many high-dimensional and high-throughput biological datasets have complex sample correlation structures, which include longitudinal and multiple tissue data, as well as data with multiple treatment conditions or related individuals. These data, as well as nearly all high-throughput “omic” data, are influenced by technical and biological factors unknown to the researcher, which, if unaccounted for ...
DOI:
10.1080/01621459.2020.1769635