Stats Pub Date : 2022-12-16 DOI: 10.3390/stats5040081

D. Griffith, R. Plant

{"title":"Statistical Analysis in the Presence of Spatial Autocorrelation: Selected Sampling Strategy Effects","authors":"D. Griffith, R. Plant","doi":"10.3390/stats5040081","DOIUrl":"https://doi.org/10.3390/stats5040081","url":null,"abstract":"Fundamental to most classical data collection sampling theory development is the random drawings assumption requiring that each targeted population member has a known sample selection (i.e., inclusion) probability. Frequently, however, unrestricted random sampling of spatially autocorrelated data is impractical and/or inefficient. Instead, randomly choosing a population subset accounts for its exhibited spatial pattern by utilizing a grid, which often provides improved parameter estimates, such as the geographic landscape mean, at least via its precision. Unfortunately, spatial autocorrelation latent in these data can produce a questionable mean and/or standard error estimate because each sampled population member contains information about its nearby members, a data feature explicitly acknowledged in model-based inference, but ignored in design-based inference. This autocorrelation effect prompted the development of formulae for calculating an effective sample size (i.e., the equivalent number of sample selections from a geographically randomly distributed population that would yield the same sampling error) estimate. Some researchers recently challenged this and other aspects of spatial statistics as being incorrect/invalid/misleading. This paper seeks to address this category of misconceptions, demonstrating that the effective geographic sample size is a valid and useful concept regardless of the inferential basis invoked. Its spatial statistical methodology builds upon the preceding ingredients.","PeriodicalId":93142,"journal":{"name":"Stats","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48024679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Robust Testing of Paired Outcomes Incorporating Covariate Effects in Clustered Data with Informative Cluster Size 包含协变量效应的成对结果的稳健性检验

Stats Pub Date : 2022-12-14 DOI: 10.3390/stats5040080

S. Dutta

{"title":"Robust Testing of Paired Outcomes Incorporating Covariate Effects in Clustered Data with Informative Cluster Size","authors":"S. Dutta","doi":"10.3390/stats5040080","DOIUrl":"https://doi.org/10.3390/stats5040080","url":null,"abstract":"Paired outcomes are common in correlated clustered data where the main aim is to compare the distributions of the outcomes in a pair. In such clustered paired data, informative cluster sizes can occur when the number of pairs in a cluster (i.e., a cluster size) is correlated to the paired outcomes or the paired differences. There have been some attempts to develop robust rank-based tests for comparing paired outcomes in such complex clustered data. Most of these existing rank tests developed for paired outcomes in clustered data compare the marginal distributions in a pair and ignore any covariate effect on the outcomes. However, when potentially important covariate data is available in observational studies, ignoring these covariate effects on the outcomes can result in a flawed inference. In this article, using rank based weighted estimating equations, we propose a robust procedure for covariate effect adjusted comparison of paired outcomes in a clustered data that can also address the issue of informative cluster size. Through simulated scenarios and real-life neuroimaging data, we demonstrate the importance of considering covariate effects during paired testing and robust performances of our proposed method in covariate adjusted paired comparisons in complex clustered data settings.","PeriodicalId":93142,"journal":{"name":"Stats","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47985565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Extracting Proceedings Data from Court Cases with Machine Learning 用机器学习从法庭案件中提取诉讼数据

Stats Pub Date : 2022-12-13 DOI: 10.3390/stats5040079

Bruno Mathis

引用次数: 0

Regression Models for Lifetime Data: An Overview 寿命数据的回归模型：综述

Stats Pub Date : 2022-12-07 DOI: 10.3390/stats5040078

C. Caroni

引用次数: 0

The Lookup Table Regression Model for Histogram-Valued Symbolic Data 直方图值符号数据的查找表回归模型

Stats Pub Date : 2022-12-04 DOI: 10.3390/stats5040077

M. Ichino

引用次数: 0

Addressing Disparities in the Propensity Score Distributions for Treatment Comparisons from Observational Studies 解决观察性研究治疗比较倾向评分分布的差异

Stats Pub Date : 2022-12-02 DOI: 10.3390/stats5040076

Tingting Zhou, M. Elliott, R. Little

{"title":"Addressing Disparities in the Propensity Score Distributions for Treatment Comparisons from Observational Studies","authors":"Tingting Zhou, M. Elliott, R. Little","doi":"10.3390/stats5040076","DOIUrl":"https://doi.org/10.3390/stats5040076","url":null,"abstract":"Propensity score (PS) based methods, such as matching, stratification, regression adjustment, simple and augmented inverse probability weighting, are popular for controlling for observed confounders in observational studies of causal effects. More recently, we proposed penalized spline of propensity prediction (PENCOMP), which multiply-imputes outcomes for unassigned treatments using a regression model that includes a penalized spline of the estimated selection probability and other covariates. For PS methods to work reliably, there should be sufficient overlap in the propensity score distributions between treatment groups. Limited overlap can result in fewer subjects being matched or in extreme weights causing numerical instability and bias in causal estimation. The problem of limited overlap suggests (a) defining alternative estimands that restrict inferences to subpopulations where all treatments have the potential to be assigned, and (b) excluding or down-weighting sample cases where the propensity to receive one of the compared treatments is close to zero. We compared PENCOMP and other PS methods for estimation of alternative causal estimands when limited overlap occurs. Simulations suggest that, when there are extreme weights, PENCOMP tends to outperform the weighted estimators for ATE and performs similarly to the weighted estimators for alternative estimands. We illustrate PENCOMP in two applications: the effect of antiretroviral treatments on CD4 counts using the Multicenter AIDS cohort study (MACS) and whether right heart catheterization (RHC) is a beneficial treatment in treating critically ill patients.","PeriodicalId":93142,"journal":{"name":"Stats","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47634803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Bayesian One-Sample Test for Proportion 比例的贝叶斯单样本检验

Stats Pub Date : 2022-12-01 DOI: 10.3390/stats5040075

L. Al-Labadi, Yifan Cheng, Forough Fazeli-Asl, Kyuson Lim, Ya-Fang Weng

引用次数: 1

A Bootstrap Method for a Multiple-Imputation Variance Estimator in Survey Sampling 调查抽样中多脉冲方差估计的Bootstrap方法

Stats Pub Date : 2022-11-29 DOI: 10.3390/stats5040074

Lili Yu, Yichuan Zhao

引用次数: 0

Assessing Regional Entrepreneurship: A Bootstrapping Approach in Data Envelopment Analysis 评估区域企业家精神:数据包络分析中的自举方法

Stats Pub Date : 2022-11-28 DOI: 10.3390/stats5040073

I. Tsolas