Biometrika

最新文章

• Construction of maximin distance Latin squares and related Latin hypercube designs AI翻译
  作者：Xiao, Qian; Xu, Hongquan
  作者单位：University of California System; University of California Los Angeles
  摘要：Maximin distance Latin hypercube designs are widely used in computer experiments, yet their construction is challenging. Based on number theory and finite fields, we propose three algebraic methods to construct maximin distance Latin squares as special Latin hypercube designs. We develop lower bounds on their minimum distances. The resulting Latin squares and related Latin hypercube designs have larger minimum distances than existing ones, and are especially appealing for high-dimensional appl...
• A note on multiple imputation under complex sampling AI翻译
  作者：Kim, J. K.; Yang, S.
  作者单位：Iowa State University; North Carolina State University
  摘要：Multiple imputation is popular for handling item nonresponse in survey sampling. Current multiple imputation techniques with complex survey data assume that the sampling design is ignorable. In this paper, we propose a new multiple imputation procedure for parametric inference without this assumption. Instead of using the sample-data likelihood, we use the sampling distribution of the pseudo maximum likelihood estimator to derive the posterior distribution of the parameters. The asymptotic pro...
• Joint sufficient dimension reduction and estimation of conditional and average treatment effects AI翻译
  作者：Huang, Ming-Yueh; Chan, Kwun Chuen Gary
  作者单位：University of Washington; University of Washington Seattle
  摘要：The estimation of treatment effects based on observational data usually involves multiple confounders, and dimension reduction is often desirable and sometimes inevitable. We first clarify the definition of a central subspace that is relevant for the efficient estimation of average treatment effects. A criterion is then proposed to simultaneously estimate the structural dimension, the basis matrix of the joint central subspace, and the optimal bandwidth for estimating the conditional treatment...
• Multiple robustness in factorized likelihood models AI翻译
  作者：Molina, J.; Rotnitzky, A.; Sued, M.; Robins, J. M.
  作者单位：University of Buenos Aires; Universidad Torcuato Di Tella; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：We consider inference under a nonparametric or semiparametric model with likelihood that factorizes as the product of two or more variation-independent factors. We are interested in a finitedimensional parameter that depends on only one of the likelihood factors and whose estimation requires the auxiliary estimation of one or several nuisance functions. We investigate general structures conducive to the construction of so-called multiply robust estimating functions, whose computation requires ...
• Identification and estimation of causal effects with outcomes truncated by death AI翻译
  作者：Wang, Linbo; Zhou, Xiao-Hua; Richardson, Thomas S.
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
  摘要：It is common in medical studies that the outcome of interest is truncated by death, meaning that a subject has died before the outcome could be measured. In this case, restricted analysis among survivors may be subject to selection bias. Hence, it is of interest to estimate the survivor average causal effect, defined as the average causal effect among the subgroup consisting of subjects who would survive under either exposure. In this paper, we consider the identification and estimation proble...
• An improved and explicit surrogate variable analysis procedure by coefficient adjustment AI翻译
  作者：Lee, Seunggeun; Sun, Wei; Wright, Fred A.; Zou, Fei
  作者单位：University of Michigan System; University of Michigan; Fred Hutchinson Cancer Center; North Carolina State University; State University System of Florida; University of Florida
  摘要：Unobserved environmental, demographic and technical factors can adversely affect the estimation and testing of the effects of primary variables. Surrogate variable analysis, proposed to tackle this problem, has been widely used in genomic studies. To estimate hidden factors that are correlated with the primary variables, surrogate variable analysis performs principal component analysis either on a subset of features or on all features, but weighting each differently. However, existing approach...
• Optimal Bayes classifiers for functional data and density ratios AI翻译
  作者：Dai, Xiongtao; Mueller, Hans-Georg; Yao, Fang
  作者单位：University of California System; University of California Davis; Peking University; Peking University
  摘要：Bayes classifiers for functional data pose a challenge. One difficulty is that probability density functions do not exist for functional data, so the classical Bayes classifier using density quotients needs to be modified. We propose to use density ratios of projections onto a sequence of eigenfunctions that are common to the groups to be classified. The density ratios are then factorized into density ratios of individual projection scores, reducing the classification problem to obtaining a se...
• Dependent generalized functional linear models AI翻译
  作者：Jadhav, S.; Koul, H. L.; Lu, Q.
  作者单位：Michigan State University; Michigan State University
  摘要：This paper considers testing for no effect of functional covariates on response variables in multivariate regression. We use generalized estimating equations to determine the underlying parameters and establish their joint asymptotic normality. This is then used to test the significance of the effect of predictors on the vector of response variables. Simulations demonstrate the importance of considering existing correlation structures in the data. To explore the effect of treating genetic data...
• Bayesian local extremum splines AI翻译
  作者：Wheeler, M. W.; Dunson, D. B.; Herring, A. H.
  作者单位：Centers for Disease Control & Prevention - USA; National Institute for Occupational Safety & Health (NIOSH); Duke University
  摘要：We consider shape- restricted nonparametric regression on a closed set X. R, where it is reasonable to assume that the function has no more than H local extrema interior to X. Following a Bayesian approach we develop a nonparametric prior over a novel class of local extremum splines. This approach is shown to be consistent when modelling any continuously differentiable function within the class considered, and we use it to develop methods for testing hypotheses on the shape of the curve. Sampl...
• Regression modelling on stratified data with the lasso AI翻译
  作者：Ollier, E.; Viallon, V.
  作者单位：Ecole Normale Superieure de Lyon (ENS de LYON); Universite Gustave-Eiffel; Universite Claude Bernard Lyon 1
  摘要：We consider the estimation of regression models on strata defined using a categorical covariate, in order to identify interactions between this categorical covariate and the other predictors. A basic approach requires the choice of a reference stratum. We show that the performance of a penalized version of this approach depends on this arbitrary choice, and propose an approach that bypasses this at almost no additional computational cost. Regarding model selection consistency, our proposal mim...