Michael P. Fix  Bailey R. Fairbanks 《Social science quarterly》2020,101(2):811-824

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Tyler P. Reinagel  Christopher A. Cooper 《Social science quarterly》2020,101(2):427-438

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M. Templ  J. Gussenbauer  P. Filzmoser 《Journal of applied statistics》2020,47(7):1144

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J. Mazucheli  A. F. B. Menezes  L. B. Fernandes  R. P. de Oliveira  M. E. Ghitany 《Journal of applied statistics》2020,47(6):954

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Aisha Betoko  Chike Nwabuo  Bharath Ambale Venkatesh  Erin P. Ricketts  Sejong Bae  Colin Wu  Samuel S. Gidding  Kiang Liu  Joo A. C. Lima  Christopher Cox 《Journal of applied statistics》2020,47(7):1315

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Michelle Xia  P. Richard Hahn  Paul Gustafson 《Revue canadienne de statistique》2020,48(4):731-750

This article considers misclassification of categorical covariates in the context of regression analysis; if unaccounted for, such errors usually result in mis-estimation of model parameters. With the presence of additional covariates, we exploit the fact that explicitly modelling non-differential misclassification with respect to the response leads to a mixture regression representation. Under the framework of mixture of experts, we enable the reclassification probabilities to vary with other covariates, a situation commonly caused by misclassification that is differential on certain covariates and/or by dependence between the misclassified and additional covariates. Using Bayesian inference, the mixture approach combines learning from data with external information on the magnitude of errors when it is available. In addition to proving the theoretical identifiability of the mixture of experts approach, we study the amount of efficiency loss resulting from covariate misclassification and the usefulness of external information in mitigating such loss. The method is applied to adjust for misclassification on self-reported cocaine use in the Longitudinal Studies of HIV-Associated Lung Infections and Complications.  相似文献   

    

Yuying Li  Kristina P. Sendova 《统计学通讯:理论与方法》2020,49(13):3238-3256

Abstract

In this paper, we introduce a surplus process involving a compound Poisson counting process, which is a generalization of the classical ruin model where the claim-counting process is a homogeneous Poisson process. The incentive is to model batch arrival of claims using a counting process that is based on a compound distribution. This reduces the difficulty of modeling claim amounts and is consistent with industrial data. Recursive formula, some properties and relevant main ruin theory results are provided. Further, we consider applications involving zero-truncated negative binomial and zero-truncated binomial batch arrivals when the claim amounts follow exponential or Erlang distribution.  相似文献   

    

Donald R. McClure  Michael P. Marino 《Social Studies》2020,111(3):109-122

Abstract

This article discusses how the history of sports can serve as way to understand abstract concepts associated with local history and social studies education. An introductory discussion outlines how sports can engage and interest students, focusing especially on ideas related to history thinking (such as change and continuity). A case study using the Brooklyn Dodgers’ move to Los Angeles is used to illustrate these ideas, focusing on themes associated with suburbanization and urban renewal. A lesson plan is also provided.  相似文献   

    

Valentijn M.T. de Jong  Karel G.M. Moons  Richard D. Riley  Catrin Tudur Smith  Anthony G. Marson  Marinus J.C. Eijkemans  Thomas P.A. Debray 《Research Synthesis Methods》2020,11(2):148-168

Many randomized trials evaluate an intervention effect on time‐to‐event outcomes. Individual participant data (IPD) from such trials can be obtained and combined in a so‐called IPD meta‐analysis (IPD‐MA), to summarize the overall intervention effect. We performed a narrative literature review to provide an overview of methods for conducting an IPD‐MA of randomized intervention studies with a time‐to‐event outcome. We focused on identifying good methodological practice for modeling frailty of trial participants across trials, modeling heterogeneity of intervention effects, choosing appropriate association measures, dealing with (trial differences in) censoring and follow‐up times, and addressing time‐varying intervention effects and effect modification (interactions).We discuss how to achieve this using parametric and semi‐parametric methods, and describe how to implement these in a one‐stage or two‐stage IPD‐MA framework. We recommend exploring heterogeneity of the effect(s) through interaction and non‐linear effects. Random effects should be applied to account for residual heterogeneity of the intervention effect. We provide further recommendations, many of which specific to IPD‐MA of time‐to‐event data from randomized trials examining an intervention effect.We illustrate several key methods in a real IPD‐MA, where IPD of 1225 participants from 5 randomized clinical trials were combined to compare the effects of Carbamazepine and Valproate on the incidence of epileptic seizures.  相似文献   

    

Fernando P. Hartwig  George Davey Smith  Amand F. Schmidt  Jonathan A. C. Sterne  Julian P. T. Higgins  Jack Bowden 《Research Synthesis Methods》2020,11(3):397-412

Meta‐analyses based on systematic literature reviews are commonly used to obtain a quantitative summary of the available evidence on a given topic. However, the reliability of any meta‐analysis is constrained by that of its constituent studies. One major limitation is the possibility of small‐study effects, when estimates from smaller and larger studies differ systematically. Small‐study effects may result from reporting biases (ie, publication bias), from inadequacies of the included studies that are related to study size, or from reasons unrelated to bias. We propose two estimators based on the median and mode to increase the reliability of findings in a meta‐analysis by mitigating the influence of small‐study effects. By re‐examining data from published meta‐analyses and by conducting a simulation study, we show that these estimators offer robustness to a range of plausible bias mechanisms, without making explicit modelling assumptions. They are also robust to outlying studies without explicitly removing such studies from the analysis. When meta‐analyses are suspected to be at risk of bias because of small‐study effects, we recommend reporting the mean, median and modal pooled estimates.  相似文献