-
作者：Xia, Yingcun
作者单位：National University of Singapore
摘要：Lack-of-fit checking for parametric and semiparametric models is essential in reducing misspecification. The efficiency of most existing model-checking methods drops rapidly as the dimension of the covariates increases. We propose to check a model by projecting the fitted residuals along a direction that adapts to the systematic departure of the residuals from the desired pattern. Consistency of the method is proved for parametric and semiparametric regression models. A bootstrap implementatio...
-
作者：Chen, Song Xi; Peng, Liang; Qin, Ying-Li
作者单位：Iowa State University; University System of Georgia; Georgia Institute of Technology
摘要：We evaluate the effects of data dimension on the asymptotic normality of the empirical likelihood ratio for high-dimensional data under a general multivariate model. Data dimension and dependence among components of the multivariate random vector affect the empirical likelihood directly through the trace and the eigenvalues of the covariance matrix. The growth rates to infinity we obtain for the data dimension improve the rates of Hjort et al. (2008).
-
作者：Conti, S.; Gosling, J. P.; Oakley, J. E.; O'Hagan, A.
作者单位：Health Protection Agency; Food & Environment Research Agency; University of Sheffield
摘要：Computer codes are used in scientific research to study and predict the behaviour of complex systems. Their run times often make uncertainty and sensitivity analyses impractical because of the thousands of runs that are conventionally required, so efficient techniques have been developed based on a statistical representation of the code. The approach is less straightforward for dynamic codes, which represent time-evolving systems. We develop a novel iterative system to build a statistical mode...
-
作者：Chernozhukov, V.; Fernandez-Val, I.; Galichon, A.
作者单位：Massachusetts Institute of Technology (MIT); Boston University; Institut Polytechnique de Paris; Ecole Polytechnique
摘要：Suppose that a target function is monotonic and an available original estimate of this target function is not monotonic. Rearrangements, univariate and multivariate, transform the original estimate to a monotonic estimate that always lies closer in common metrics to the target function. Furthermore, suppose an original confidence interval, which covers the target function with probability at least 1-alpha, is defined by an upper and lower endpoint functions that are not monotonic. Then the rea...
-
作者：Crump, Richard K.; Hotz, V. Joseph; Imbens, Guido W.; Mitnik, Oscar A.
作者单位：University of California System; University of California Berkeley; Duke University; Harvard University; University of Miami
摘要：Estimation of average treatment effects under unconfounded or ignorable treatment assignment is often hampered by lack of overlap in the covariate distributions between treatment groups. This lack of overlap can lead to imprecise estimates, and can make commonly used estimators sensitive to the choice of specification. In such cases researchers have often used ad hoc methods for trimming the sample. We develop a systematic approach to addressing lack of overlap. We characterize optimal subsamp...
-
作者：Beerenwinkel, N.; Sullivant, S.
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich; North Carolina State University
摘要：We introduce and analyze a waiting time model for the accumulation of genetic changes. The continuous-time conjunctive Bayesian network is defined by a partially ordered set of mutations and by the rate of fixation of each mutation. The partial order encodes constraints on the order in which mutations can fixate in the population, shedding light on the mutational pathways underlying the evolutionary process. We study a censored version of the model and derive equations for an em algorithm to p...
-
作者：Luo, Xiaodong; Tsai, Wei Yann; Xu, Qiang
作者单位：Icahn School of Medicine at Mount Sinai; Columbia University; US Food & Drug Administration (FDA)
摘要：By embedding the missing covariate data into a left-truncated and right-censored survival model, we propose a new class of weighted estimating functions for the Cox regression model with missing covariates. The resulting estimators, called the pseudo-partial likelihood estimators, are shown to be consistent and asymptotically normal. A simulation study demonstrates that, compared with the popular inverse-probability weighted estimators, the new estimators perform better when the observation pr...
-
作者：Wong, C. S.; Chan, W. S.; Kam, P. L.
作者单位：Chinese University of Hong Kong; University of Hong Kong
摘要：We introduce the class of Student t-mixture autoregressive models, which is promising for financial time series modelling. The model is able to capture serial correlations, time-varying means and volatilities, and the shape of the conditional distributions can be time varied from short-tailed to long-tailed, or from unimodal to multimodal. The use of t-distributed errors in each component of the model allows conditional leptokurtic distributions that account for the commonly observed excess un...
-
作者：Wu, S.; Shen, X.; Geyer, C. J.
作者单位：University of Minnesota System; University of Minnesota Twin Cities
摘要：Several sparseness penalties have been suggested for delivery of good predictive performance in automatic variable selection within the framework of regularization. All assume that the true model is sparse. We propose a penalty, a convex combination of the L-1- and L-infinity-norms, that adapts to a variety of situations including sparseness and nonsparseness, grouping and nongrouping. The proposed penalty performs grouping and adaptive regularization. In addition, we introduce a novel homotop...
-
作者：Fewster, R. M.; Jupp, P. E.
作者单位：University of Auckland; University of St Andrews
摘要：Many models for biological populations, including simple mark-recapture models and distance sampling models, involve a binomially distributed number, n, of observations x(1), ..., x(n) on members of a population of size N. Two popular estimators of (N, theta), where theta is a vector parameter, are the maximum likelihood estimator ((N) over cap, (theta) over cap) and the conditional maximum likelihood estimator ((N) over cap (c) (theta) over cap (c)) based on the conditional distribution of x(...
