Biometrika

最新文章

• Predicting future responses based on possibly mis-specified working models AI翻译
  作者：Cai, Tianxi; Tian, Lu; Solomon, Scott D.; Wei, L. J.
  作者单位：Harvard University; Northwestern University; Harvard University; Harvard University Medical Affiliates; Brigham & Women's Hospital
  摘要：Under a general regression setting, we propose an optimal unconditional prediction procedure for future responses. The resulting prediction intervals or regions have a desirable average coverage level over a set of covariate vectors of interest. When the working model is not correctly specified, the traditional conditional prediction method is generally invalid. On the other hand, one can empirically calibrate the above unconditional procedure and also obtain its crossvalidated counterpart. Va...
• Conditional properties of unconditional parametric bootstrap procedures for inference in exponential families AI翻译
  作者：Diciccio, Thomas J.; Young, Alastair
  作者单位：Cornell University; Imperial College London
  摘要：Higher-order inference about a scalar parameter in the presence of nuisance parameters can be achieved by bootstrapping, in circumstances where the parameter of interest is a component of the canonical parameter in a full exponential family. The optimal test, which is approximated, is a conditional one based on conditioning on the sufficient statistic for the nuisance parameter. A bootstrap procedure that ignores the conditioning is shown to have desirable conditional properties in providing t...
• Semiparametric model-based inference in the presence of missing responses AI翻译
  作者：Wang, Qihua; Dai, Pengjie
  作者单位：Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS
  摘要：We consider a semiparametric model that parameterizes the conditional density of the response, given covariates, but allows the marginal distribution of the covariates to be completely arbitrary. Responses may be missing. A likelihood-based imputation estimator and a semi-empirical-likelihood-based estimator for the parameter vector describing the conditional density are defined and proved to be asymptotically normal. Semi-empirical loglikelihood functions for the parameter vector and the resp...
• Analysis of least absolute deviation AI翻译
  作者：Chen, Kani; Ying, Zhiliang; Zhang, Hong; Zhao, Lincheng
  作者单位：Hong Kong University of Science & Technology; Columbia University; Chinese Academy of Sciences; University of Science & Technology of China, CAS
  摘要：We develop a unified L-1-based analysis-of-variance-type method for testing linear hypotheses. Like the classical L-2-based analysis of variance, the method is coordinate-free in the sense that it is invariant under any linear transformation of the covariates or regression parameters. Moreover, it allows singular design matrices and heterogeneous error terms. A simple approximation using stochastic perturbation is proposed to obtain cut-off values for the resulting test statistics. Both test s...
• Distortion of effects caused by indirect confounding AI翻译
  作者：Wermuth, Nanny; Cox, D. R.
  作者单位：Chalmers University of Technology; University of Oxford
  摘要：Undetected confounding may severely distort the effect of an explanatory variable on a response variable, as defined by a stepwise data-generating process. The best known type of distortion, which we call direct confounding, arises from an unobserved explanatory variable common to a response and its main explanatory variable of interest. It is relevant mainly for observational studies, since it is avoided by successful randomization. By contrast, indirect confounding, which we identify in this...
• A goodness-of-fit test for inhomogeneous spatial Poisson processes AI翻译
  作者：Guan, Yongtao
  作者单位：Yale University
  摘要：We introduce a formal testing procedure to assess the fit of an inhomogeneous spatial Poisson process model, based on a discrepancy measure function Dc( t; theta) that is constructed from residuals obtained from the fitted model. We derive the asymptotic distributional properties of Dc( t; theta) and develop a test statistic based on them. Our test statistic has a limiting standard normal distribution, so that the test can be performed by simply comparing the test statistic with readily availa...
• Improving the efficiency of the log-rank test using auxiliary covariates AI翻译
  作者：Lu, Xiaomin; Tsiatis, Anastasios A.
  作者单位：State University System of Florida; University of Florida; North Carolina State University
  摘要：Under the assumption of proportional hazards, the log-rank test is optimal for testing the null hypothesis H-0 : beta = 0, where beta denotes the logarithm of the hazard ratio. However, if there are additional covariates that correlate with survival times, making use of their information will increase the efficiency of the log-rank test. We apply the theory of semiparametrics to characterize a class of regular and asymptotically linear estimators for beta when auxiliary covariates are incorpor...
• Empirical and counterfactual conditions for sufficient cause interactions AI翻译
  作者：Vanderweele, Tyler J.; Robins, James M.
  作者单位：University of Chicago; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：Sufficient-component causes are discussed within the deterministic potential outcomes framework so as to formalize notions of sufficient causes, synergism and sufficient cause interactions. Doing so allows for the derivation of counterfactual and empirical conditions for detecting the presence of sufficient cause interactions. The conditions are novel in that, unlike other conditions in the literature, they make no assumption about monotonicity. The conditions can also be generalized and the c...
• Studentization and deriving accurate p-values AI翻译
  作者：Fraser, D. A. S.; Rousseau, Judith
  作者单位：University of Toronto; Universite PSL; Universite Paris-Dauphine
  摘要：We have a statistic for assessing an observed data point relative to a statistical model but find that its distribution function depends on the parameter. To obtain the corresponding p-value, we require the minimally modified statistic that is ancillary; this process is called Studentization. We use recent likelihood theory to develop a maximal third-order ancillary; this gives immediately a candidate Studentized statistic. We show that the corresponding p-value is higher-order Un(0, 1), is eq...
• Additive partial linear models with measurement errors AI翻译
  作者：Liang, Hua; Thurston, Sally W.; Ruppert, David; Apanasovich, Tatiyana; Hauser, Russ
  作者单位：University of Rochester; Cornell University; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：We consider statistical inference for additive partial linear models when the linear covariate is measured with error. We propose attenuation-to-correction and simulation-extrapolation, SIMEX, estimators of the parameter of interest. It is shown that the first resulting estimator is asymptotically normal and requires no undersmoothing. This is an advantage of our estimator over existing backfitting-based estimators for semiparametric additive models which require undersmoothing of the nonparam...