450期

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• Power calculations for data missing by design: Applications to a follow-up study of lead exposure and attention AI翻译
  作者：Brown, CH; Indurkhya, A; Kellam, SG
  作者单位：State University System of Florida; University of South Florida; Michigan State University; Maryland Department of Health & Mental Hygiene; Johns Hopkins University
  摘要：Longitudinal designs often change at critical times based on available funding, staffing, scientific opportunities, and subjects. This article presents three levels of investigation into missingness by design in a partially completed longitudinal study: missingness that is completely at random (MCAR), at random (MAR), and nonignorable (MN). We first derive new expressions for the asymptotic variance and power based on multivariate normal data that are either MCAR or missing by design (MAR). Th...
• Causal inference without counterfactuals AI翻译
  作者：Dawid, AP
  作者单位：University of London; University College London
  摘要：A popular approach to the framing and answering of causal questions relies on the idea of counterfactuals: outcomes that would have been observed had the world developed differently; for example, if the patient had received a different treatment. By definition one can never observe such quantities, nor assess empirically the validity of any modeling assumptions made about them, even though one's conclusions may be sensitive to these assumptions. Here I argue that for making inference about the...
• On profile likelihood AI翻译
  作者：Murphy, SA; Van der Vaart, AW
  作者单位：University of Michigan System; University of Michigan; Vrije Universiteit Amsterdam
  摘要：We show that semiparametric profile likelihoods, where the nuisance parameter has been profiled out, behave like ordinary likelihoods in that they have a quadratic expansion. In this expansion the score function and the Fisher information are replaced by-the efficient score function and efficient Fisher information. The expansion may be used, among others, to prove the asymptotic normality of the maximum likelihood estimator, to derive the asymptotic chi-squared distribution of the log-likelih...
• Demography: Past, present, and future AI翻译
  作者：Xie, Y
  作者单位：University of Michigan System; University of Michigan
• Fitting population dynamics models to count and cull data using sequential importance sampling AI翻译
  作者：Trenkel, VM; Elston, DA; Buckland, ST
  作者单位：Ifremer; James Hutton Institute; University of St Andrews
  摘要：For prudent wildlife management based on population dynamics models, it is important to incorporate parameter uncertainty into the management advice. Much parameter uncertainty originates when It Is not possible to parameterize the population management model for a population of interest using data from that population alone. Instead, information about parameter values obtained from other populations of the same species, or even from similar species, must be used. In addition, the age structur...
• On profile likelihood - Comment AI翻译
  作者：Robins, JM; Rotnitzky, A; van der Laan, M
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; University of California System; University of California Berkeley
• Political methodology: A welcoming discipline AI翻译
  作者：Beck, NL
  作者单位：University of California System; University of California San Diego
• Combining propensity score matching with additional adjustments for prognostic covariates AI翻译
  作者：Rubin, DB; Thomas, N
  作者单位：Harvard University; Bristol-Myers Squibb
  摘要：Propensity score matching refers to a class of multivariate methods used in comparative studies to construct treated and matched control samples that have similar distributions on many covariates. This matching is the observational study analog of randomization in ideal experiments, but is far less complete as it can only balance the distribution of observed covariates, whereas randomization balances the distribution of all covariates, both observed and unobserved. An important feature of prop...
• Asymptotic behavior of linear permutation tests under general alternatives, with application to test selection and study design AI翻译
  作者：Weinberg, JM; Lagakos, SW
  作者单位：Boston University; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：Tests based on the permutation of observations are a common and attractive method of comparing two groups of outcomes. in part because they retain proper test size with minimal assumptions and can have high efficiency toward specific alternatives of interest. In addition, permutation tests may be used with discrete or categorical outcomes, for which linear rank tests are not designed. Permutation tests are now increasingly used to analyze discrete or continuous responses that themselves are fu...
• Nonparametric function estimation for clustered data when the predictor is measured without/with error AI翻译
  作者：Lin, XH; Carroll, RJ
  作者单位：University of Michigan System; University of Michigan; Texas A&M University System; Texas A&M University College Station; Texas A&M University System; Texas A&M University College Station
  摘要：We consider local polynomial kernel regression with a single covariate for clustered data using estimating equations. We assume that at most m < infinity observations are available on each cluster. In the case of random regressors. with no measurement error in the predictor, we show that it is generally the best strategy to ignore entirely the correlation structure within each cluster and instead pretend that all observations are independent. In the Further special case of longitudinal data on...