AMERICAN STATISTICIAN美国统计学家
AMERICAN STATISTICIAN(英文缩写 AM STAT),ISSN 0003-1305,eISSN 1537-2731,中文译名:美国统计学家 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-09-27 至 2026-09-27，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 8.325 | Q1 |
| 2022 | 1.800 | Q2 |
| 2023 | 1.800 | Q1 |
| 2024 | 2.100 | Q1 |
| 2025 | 2.200 | Q1 |
AMERICAN STATISTICIAN 最新收录文献
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1. Conceptualizing Experimental Controls Using the Potential Outcomes Framework.
PMID:日期:2025-10-16The goal of a controlled experiment is to remove unwanted variation when estimating the causal effect of the intervention. Experiments conducted in the basic sciences frequently achieve this goal using experimental controls, such as "negative" and "positive" controls, which are additional experimental components designed to detect systematic sources of variation. We introduce a taxonomy of clear, mathematically-precise definitions of experimental controls using the potential outcomes framework. These definitions are intended for pedagogical purposes: they may be used by educators to ensure that their students adhere to good statistical practice, and may also be useful for communication with practitioners who are less familiar with statistical concepts. We define three types of experimental controls based on assumptions about potential outcomes: treatment, outcome, and contrast controls. After each type of control is introduced, we provide examples of its use. We also discuss experimental controls as tools for researchers to use in designing experiments and detecting potential design flaws, such as identifying unwanted variation. We believe that experimental controls are powerful yet underutilized tools for reproducible, replicable, rigorous, and transparent research.
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2. A Cornucopia of Maximum Likelihood Algorithms.
PMID:日期:2025-08-04Classroom expositions of maximum likelihood estimation (MLE) rely on traditional calculus methods to construct analytic solutions. This creates in students a false sense of the ease with which MLE problems can be attacked. In a nod to reality, some teachers mention and apply Newton's method, Fisher scoring, and the expectation-maximization (EM) algorithm. Although preferable to leaving students in a state of ignorance, such brief expositions ultimately fail to expose the full body of relevant techniques. Some of these techniques extend more readily to high-dimensional data problems than Newton's method and scoring. The current paper emphasizes block ascent and descent, profile likelihoods, the minorization-maximization (MM) principle, and their creative combination. These themes are put to work in readable Julia code to solve several MLE problems.
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3. A Multiple Imputation Approach for the Cumulative Incidence, with Implications for Variance Estimation.
PMID:日期:2025-08-01We present an alternative approach to estimating the cumulative incidence function that uses non-parametric multiple imputation to reduce the problem to that of estimating a binomial proportion. In the standard competing risks setting, we show mathematically and empirically that our imputation-based estimator is equivalent to the Aalen-Johansen estimator of the cumulative incidence given a sufficient number of imputations. However, our approach allows for the use of a wider variety of methods for the analysis of binary outcomes, including preferred options for uncertainty estimation. While we focus on the cumulative incidence function, the multiple imputation approach likely extends to more complex problems in competing risks.
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4. Laplace's law of succession estimator and M-statistics.
PMID:日期:2025-08-01The classic formula for estimating the binomial probability as the proportion of successes contradicts common sense for extreme probabilities when the event never occurs or occurs every time. Laplace's law of succession estimator, one of the first applications of Bayesian statistics, has been around for over 250 years and resolves the paradoxes, although rarely discussed in modern statistics texts. This work aims to introduce a new theory for exact optimal statistical inference using Laplace's law of succession estimator as a motivating example. We prove that this estimator may be viewed from a different theoretical perspective as the limit point of the short confidence interval on the double-log scale when the confidence level approaches zero. This motivating example paves the road to the definition of an estimator as the inflection point on the cumulative distribution function as a function of the parameter given the observed statistic. This estimator has the maximum infinitesimal probability of the coverage of the unknown parameter and, therefore, is called the maximum concentration (MC) estimator as a part of a more general M-statistics theory. The new theory is illustrated with exact optimal confidence intervals for the normal standard deviation and the respective MC estimators.
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5. An Example to Illustrate Randomized Trial Estimands and Estimators.
PMID:日期:2025-01-01Recently, the International Conference on Harmonisation finalized an estimand framework for randomized trials that was adopted by regulatory bodies worldwide. The framework introduced five strategies for handling post-randomization events; namely the treatment policy, composite variable, while on treatment, hypothetical and principal stratum estimands. We describe an illustrative example to elucidate the difference between these five strategies for handling intercurrent events and provide an estimation technique for each. Specifically, we consider the intercurrent event of treatment discontinuation and introduce potential outcome notation to describe five estimands and corresponding estimators: 1) an intention-to-treat estimator of the total effect of a treatment policy; 2) an intention-to-treat estimator of a composite of the outcome and remaining on treatment; 3) a per-protocol estimator of the outcome in individuals observed to remain on treatment; 4) a g-computation estimator of a hypothetical scenario that all individuals remain on treatment; and 5) a principal stratum estimator of the treatment effect in individuals who would remain on treatment under the experimental condition. Additional insight is provided by defining situations where certain estimands are equal, and by studying the while on treatment strategy under repeated outcome measures. We highlight relevant causal inference literature to enable adoption in practice.
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6. Counternull sets in randomized experiments.
PMID:日期:2025-01-01Consider a study whose primary results are "not statistically significant". How often does it lead to the following published conclusion that "there is no effect of the treatment/exposure on the outcome"? We believe too often and that the requirement to report counternull values could help to avoid this! In statistical parlance, the null value of an estimand is a value that is distinguished in some way from other possible values, for example a value that indicates no difference between the general health status of those treated with a new drug versus a traditional drug. A counternull value is a nonnull value of that estimand that is supported by the same amount of evidence that supports the null value. Of course, such a definition depends critically on how "evidence" is defined. Here, we consider the context of a randomized experiment where evidence is summarized by the randomization-based p-value associated with a specified sharp null hypothesis. Consequently, a counternull value has the same p-value from the randomization test as does the null value; the counternull value is rarely unique, but rather comprises a of values. We explore advantages to reporting a counternull set in addition to the p-value associated with a null value; a first advantage is pedagogical, in that reporting it avoids the mistake of implicitly accepting a not-rejected null hypothesis; a second advantage is that the effort to construct a counternull set can be scientifically helpful by encouraging thought about nonnull values of estimands. Two examples are used to illustrate these ideas.
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7. Flexible Distributed Lag Models for Count Data Using mgcv.
PMID:日期:2025-01-01In this tutorial we present the use of R package mgcv to implement Distributed Lag Non-Linear Models (DLNMs) in a flexible way. Interpretation of smoothing splines as random quantities enables approximate Bayesian inference, which in turn allows uncertainty quantification and comprehensive model checking. We illustrate various modeling situations using open-access epidemiological data in conjunction with simulation experiments. We demonstrate the inclusion of temporal structures and the use of mixture distributions to allow for extreme outliers. Moreover, we demonstrate interactions of the temporal lagged structures with other covariates with different lagged periods for different covariates. Spatial structures are also demonstrated, including smooth spatial variability and Markov random fields, in addition to hierarchical formulations to allow for non-structured dependency. Posterior predictive simulation is used to ensure models verify well against the data.
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8. The R2D2 prior for generalized linear mixed models.
PMID:日期:2025-01-01In Bayesian analysis, the selection of a prior distribution is typically done by considering each parameter in the model. While this can be convenient, in many scenarios it may be desirable to place a prior on a summary measure of the model instead. In this work, we propose a prior on the model fit, as measured by a Bayesian coefficient of determination ( ), which then induces a prior on the individual parameters. We achieve this by placing a beta prior on and then deriving the induced prior on the global variance parameter for generalized linear mixed models. We derive closed-form expressions in many scenarios and present several approximation strategies when an analytic form is not possible and/or to allow for easier computation. In these situations, we suggest approximating the prior by using a generalized beta prime distribution and provide a simple default prior construction scheme. This approach is quite flexible and can be easily implemented in standard Bayesian software. Lastly, we demonstrate the performance of the method on simulated and real-world data, where the method particularly shines in high-dimensional settings, as well as modeling random effects.
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9. Sequential monitoring using the Second Generation P-Value with Type I error controlled by monitoring frequency.
PMID:日期:2025-01-01The Second Generation P-Value (SGPV) measures the overlap between an estimated interval and a composite hypothesis of parameter values. We develop a sequential monitoring scheme of the SGPV (SeqSGPV) to connect study design intentions with end-of-study inference anchored on scientific relevance. We build upon Freedman's "Region of Equivalence" (ROE) in specifying scientifically meaningful hypotheses called Pre-specified Regions Indicating Scientific Merit (PRISM). We compare PRISM monitoring versus monitoring alternative ROE specifications. Error rates are controlled through the PRISM's indifference zone around the point null and monitoring frequency strategies. Because the former is fixed due to scientific relevance, the latter is a targettable means for designing studies with desirable operating characters. An affirmation step to stopping rules improves frequency properties including the error rate, the risk of reversing conclusions under delayed outcomes, and bias.
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10. Proximal MCMC for Bayesian Inference of Constrained and Regularized Estimation.
PMID:日期:2024-01-01This paper advocates proximal Markov Chain Monte Carlo (ProxMCMC) as a flexible and general Bayesian inference framework for constrained or regularized estimation. Originally introduced in the Bayesian imaging literature, ProxMCMC employs the Moreau-Yosida envelope for a smooth approximation of the total-variation regularization term, fixes variance and regularization strength parameters as constants, and uses the Langevin algorithm for the posterior sampling. We extend ProxMCMC to be fully Bayesian by providing data-adaptive estimation of all parameters including the regularization strength parameter. More powerful sampling algorithms such as Hamiltonian Monte Carlo are employed to scale ProxMCMC to high-dimensional problems. Analogous to the proximal algorithms in optimization, ProxMCMC offers a versatile and modularized procedure for conducting statistical inference on constrained and regularized problems. The power of ProxMCMC is illustrated on various statistical estimation and machine learning tasks, the inference of which is traditionally considered difficult from both frequentist and Bayesian perspectives.