Statistics Surveys
Statistics Surveys(英文缩写 STAT SURV),ISSN 1935-7516,eISSN 1935-7516 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：2025-09-21 至 2026-09-21，按本站收录文献的发表日期统计。
指标来源：jcr_cas_ifqb
期刊简介
暂无简介。
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 未收录 | N/A |
| 2022 | 3.300 | N/A |
| 2023 | 11.000 | Q1 |
| 2024 | 15.400 | Q1 |
| 2025 | 8.200 | Q1 |
Statistics Surveys 最新收录文献
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1. Causal mediation analysis: From simple to more robust strategies for estimation of marginal natural (in)direct effects.
PMID:作者:DOI:日期:2023-01-01This paper aims to provide practitioners of causal mediation analysis with a better understanding of estimation options. We take as inputs two familiar strategies (weighting and model-based prediction) and a simple way of combining them (weighted models), and show how a range of estimators can be ge…
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2. Scalar-on-function regression for predicting distal outcomes from intensively gathered longitudinal data: Interpretability for applied scientists.
PMID:作者:DOI:日期:2019-01-01Researchers are sometimes interested in predicting a distal or external outcome (such as smoking cessation at follow-up) from the trajectory of an intensively recorded longitudinal variable (such as urge to smoke). This can be done in a semiparametric way via scalar-on-function regression. However, …
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3. A review of dynamic network models with latent variables.
PMID:作者:DOI:日期:2018-01-01We present a selective review of statistical modeling of dynamic networks. We focus on models with latent variables, specifically, the latent space models and the latent class models (or stochastic blockmodels), which investigate both the observed features and the unobserved structure of networks. W…
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4. Measuring multivariate association and beyond.
PMID:作者:DOI:日期:2016-01-01Simple correlation coefficients between two variables have been generalized to measure association between two matrices in many ways. Coefficients such as the RV coefficient, the distance covariance (dCov) coefficient and kernel based coefficients are being used by different research communities. Sc…
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5. {"_":"Analyzing complex functional brain networks: Fusing statistics and network science to understand the brain","sup":["*†"]}
PMID:作者:DOI:日期:2013-01-01Complex functional brain network analyses have exploded over the last decade, gaining traction due to their profound clinical implications. The application of network science (an interdisciplinary offshoot of graph theory) has facilitated these analyses and enabled examining the brain as an integrat…
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6. Wilcoxon-Mann-Whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules.
PMID:作者:DOI:日期:2010-01-01In a mathematical approach to hypothesis tests, we start with a clearly defined set of hypotheses and choose the test with the best properties for those hypotheses. In practice, we often start with less precise hypotheses. For example, often a researcher wants to know which of two groups generally h…
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7. Testing polynomial covariate effects in linear and generalized linear mixed models.
PMID:作者:DOI:日期:2008-01-01An important feature of linear mixed models and generalized linear mixed models is that the conditional mean of the response given the random effects, after transformed by a link function, is linearly related to the fixed covariate effects and random effects. Therefore, it is of practical importance…
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8. Log-Concavity and Strong Log-Concavity: a review.
PMID:作者:DOI:We review and formulate results concerning log-concavity and strong-log-concavity in both discrete and continuous settings. We show how preservation of log-concavity and strongly log-concavity on ℝ under convolution follows from a fundamental monotonicity result of Efron (1969). We provide a new pro…