The American Statistician最新文献_第5页

Confidence Distributions for the Autoregressive Parameter 自回归参数的置信分布

The American Statistician Pub Date : 2023-03-27 DOI: 10.1080/00031305.2023.2226184

Rolf Larsson

引用次数: 0

The Wald Confidence Interval for a Binomial p as an Illuminating “Bad” Example 二项p的Wald置信区间作为一个启发性的“坏”例子

The American Statistician Pub Date : 2023-03-21 DOI: 10.1080/00031305.2023.2183257

Per Gösta Andersson

引用次数: 1

A Characterization of Most(More) Powerful Test Statistics with Simple Nonparametric Applications 用简单的非参数应用描述大多数(更)强大的检验统计量

The American Statistician Pub Date : 2023-03-14 DOI: 10.1080/00031305.2023.2192746

A. Vexler, Alan D. Hutson

{"title":"A Characterization of Most(More) Powerful Test Statistics with Simple Nonparametric Applications","authors":"A. Vexler, Alan D. Hutson","doi":"10.1080/00031305.2023.2192746","DOIUrl":"https://doi.org/10.1080/00031305.2023.2192746","url":null,"abstract":"Data-driven most powerful tests are statistical hypothesis decision-making tools that deliver the greatest power against a fixed null hypothesis among all corresponding data-based tests of a given size. When the underlying data distributions are known, the likelihood ratio principle can be applied to conduct most powerful tests. Reversing this notion, we consider the following questions. (a) Assuming a test statistic, say T, is given, how can we transform T to improve the power of the test? (b) Can T be used to generate the most powerful test? (c) How does one compare test statistics with respect to an attribute of the desired most powerful decision-making procedure? To examine these questions, we propose one-to-one mapping of the term 'Most Powerful' to the distribution properties of a given test statistic via matching characterization. This form of characterization has practical applicability and aligns well with the general principle of sufficiency. Findings indicate that to improve a given test, we can employ relevant ancillary statistics that do not have changes in their distributions with respect to tested hypotheses. As an example, the present method is illustrated by modifying the usual t-test under nonparametric settings. Numerical studies based on generated data and a real-data set confirm that the proposed approach can be useful in practice.","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127477126","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

Out-of-sample R2: estimation and inference 样本外R2:估计和推断

The American Statistician Pub Date : 2023-02-10 DOI: 10.1080/00031305.2023.2216252

Stijn Hawinkel, W. Waegeman, Steven Maere

{"title":"Out-of-sample R2: estimation and inference","authors":"Stijn Hawinkel, W. Waegeman, Steven Maere","doi":"10.1080/00031305.2023.2216252","DOIUrl":"https://doi.org/10.1080/00031305.2023.2216252","url":null,"abstract":"Out-of-sample prediction is the acid test of predictive models, yet an independent test dataset is often not available for assessment of the prediction error. For this reason, out-of-sample performance is commonly estimated using data splitting algorithms such as cross-validation or the bootstrap. For quantitative outcomes, the ratio of variance explained to total variance can be summarized by the coefficient of determination or in-sample $R^2$, which is easy to interpret and to compare across different outcome variables. As opposed to the in-sample $R^2$, the out-of-sample $R^2$ has not been well defined and the variability on the out-of-sample $hat{R}^2$ has been largely ignored. Usually only its point estimate is reported, hampering formal comparison of predictability of different outcome variables. Here we explicitly define the out-of-sample $R^2$ as a comparison of two predictive models, provide an unbiased estimator and exploit recent theoretical advances on uncertainty of data splitting estimates to provide a standard error for the $hat{R}^2$. The performance of the estimators for the $R^2$ and its standard error are investigated in a simulation study. We demonstrate our new method by constructing confidence intervals and comparing models for prediction of quantitative $text{Brassica napus}$ and $text{Zea mays}$ phenotypes based on gene expression data.","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127156188","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: 0

Enhanced Inference for Finite Population Sampling-Based Prevalence Estimation with Misclassification Errors 基于有限总体抽样的误分类误差患病率估计的增强推理

The American Statistician Pub Date : 2023-02-07 DOI: 10.1080/00031305.2023.2250401

Lin Ge, Yuzi Zhang, L. Waller, R. Lyles

{"title":"Enhanced Inference for Finite Population Sampling-Based Prevalence Estimation with Misclassification Errors","authors":"Lin Ge, Yuzi Zhang, L. Waller, R. Lyles","doi":"10.1080/00031305.2023.2250401","DOIUrl":"https://doi.org/10.1080/00031305.2023.2250401","url":null,"abstract":"Epidemiologic screening programs often make use of tests with small, but non-zero probabilities of misdiagnosis. In this article, we assume the target population is finite with a fixed number of true cases, and that we apply an imperfect test with known sensitivity and specificity to a sample of individuals from the population. In this setting, we propose an enhanced inferential approach for use in conjunction with sampling-based bias-corrected prevalence estimation. While ignoring the finite nature of the population can yield markedly conservative estimates, direct application of a standard finite population correction (FPC) conversely leads to underestimation of variance. We uncover a way to leverage the typical FPC indirectly toward valid statistical inference. In particular, we derive a readily estimable extra variance component induced by misclassification in this specific but arguably common diagnostic testing scenario. Our approach yields a standard error estimate that properly captures the sampling variability of the usual bias-corrected maximum likelihood estimator of disease prevalence. Finally, we develop an adapted Bayesian credible interval for the true prevalence that offers improved frequentist properties (i.e., coverage and width) relative to a Wald-type confidence interval. We report the simulation results to demonstrate the enhanced performance of the proposed inferential methods.","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114532117","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: 2

MOVER-R and Penalized MOVER-R Confidence Intervals for the Ratio of Two Quantities 两个数量之比的move - r和惩罚的move - r置信区间

The American Statistician Pub Date : 2023-01-30 DOI: 10.1080/00031305.2023.2173294

Peng Wang, Yi-lei Ma, Siqi Xu, Yixin Wang, Yu Zhang, Xiangyang Lou, Ming Li, Baolin Wu, Guimin Gao, P. Yin, Nianjun Liu

{"title":"MOVER-R and Penalized MOVER-R Confidence Intervals for the Ratio of Two Quantities","authors":"Peng Wang, Yi-lei Ma, Siqi Xu, Yixin Wang, Yu Zhang, Xiangyang Lou, Ming Li, Baolin Wu, Guimin Gao, P. Yin, Nianjun Liu","doi":"10.1080/00031305.2023.2173294","DOIUrl":"https://doi.org/10.1080/00031305.2023.2173294","url":null,"abstract":"Abstract Developing a confidence interval for the ratio of two quantities is an important task in statistics because of its omnipresence in real world applications. For such a problem, the MOVER-R (method of variance recovery for the ratio) technique, which is based on the recovery of variance estimates from confidence limits of the numerator and the denominator separately, was proposed as a useful and efficient approach. However, this method implicitly assumes that the confidence interval for the denominator never includes zero, which might be violated in practice. In this article, we first use a new framework to derive the MOVER-R confidence interval, which does not require the above assumption and covers the whole parameter space. We find that MOVER-R can produce an unbounded confidence interval, just like the well-known Fieller method. To overcome this issue, we further propose the penalized MOVER-R. We prove that the new method differs from MOVER-R only at the second order. It, however, always gives a bounded and analytic confidence interval. Through simulation studies and a real data application, we show that the penalized MOVER-R generally provides a better confidence interval than MOVER-R in terms of controlling the coverage probability and the median width.","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125420368","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: 0

Consultancy Style Dissertations in Statistics and Data Science: Why and How 统计和数据科学的咨询风格论文:为什么和如何

The American Statistician Pub Date : 2023-01-11 DOI: 10.1080/00031305.2022.2163689

Serveh Sharifi Far, Vanda Inácio, D. Paulin, M. de Carvalho, Nicole Augustin, Mike Allerhand, Gail S Robertson

引用次数: 0

Bayesian Log-Rank Test 贝叶斯对数秩检验

The American Statistician Pub Date : 2023-01-06 DOI: 10.1080/00031305.2022.2161637

Jiaqi Gu, Y. Zhang, G. Yin

引用次数: 0

Semi-Structured Distributional Regression 半结构分布回归

The American Statistician Pub Date : 2023-01-03 DOI: 10.1080/00031305.2022.2164054

D. Rügamer, Chris Kolb, N. Klein

引用次数: 7

Introducing Variational Inference in Statistics and Data Science Curriculum 在统计学和数据科学课程中引入变分推理

The American Statistician Pub Date : 2023-01-03 DOI: 10.1080/00031305.2023.2232006

Vojtech Kejzlar, Jingchen Hu

{"title":"Introducing Variational Inference in Statistics and Data Science Curriculum","authors":"Vojtech Kejzlar, Jingchen Hu","doi":"10.1080/00031305.2023.2232006","DOIUrl":"https://doi.org/10.1080/00031305.2023.2232006","url":null,"abstract":"Probabilistic models such as logistic regression, Bayesian classification, neural networks, and models for natural language processing, are increasingly more present in both undergraduate and graduate statistics and data science curricula due to their wide range of applications. In this paper, we present a one-week course module for studnets in advanced undergraduate and applied graduate courses on variational inference, a popular optimization-based approach for approximate inference with probabilistic models. Our proposed module is guided by active learning principles: In addition to lecture materials on variational inference, we provide an accompanying class activity, an texttt{R shiny} app, and guided labs based on real data applications of logistic regression and clustering documents using Latent Dirichlet Allocation with texttt{R} code. The main goal of our module is to expose students to a method that facilitates statistical modeling and inference with large datasets. Using our proposed module as a foundation, instructors can adopt and adapt it to introduce more realistic case studies and applications in data science, Bayesian statistics, multivariate analysis, and statistical machine learning courses.","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127092154","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