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作者：Lin, Zhenhua; Wang, Jane-Ling; Zhong, Qixian
作者单位：National University of Singapore; University of California System; University of California Davis; Tsinghua University
摘要：Estimation of mean and covariance functions is fundamental for functional data analysis. While this topic has been studied extensively in the literature, a key assumption is that there are enough data in the domain of interest to estimate both the mean and covariance functions. We investigate mean and covariance estimation for functional snippets in which observations from a subject are available only in an interval of length strictly, and often much, shorter than the length of the whole inter...
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作者：Matsushita, Yukitoshi; Otsu, Taisuke
作者单位：Hitotsubashi University; University of London; London School Economics & Political Science
摘要：This article aims to shed light on inference problems for statistical models under alternative or nonstandard asymptotic frameworks from the perspective of the jackknife empirical likelihood. Examples include small-bandwidth asymptotics for semiparametric inference and goodness-of-fit testing, sparse-network asymptotics, many-covariates asymptotics for regression models, and many-weak-instruments asymptotics for instrumental variable regression. We first establish Wilks' theorem for the jackkn...
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作者：Dukes, Oliver; Vansteelandt, Stijn
作者单位：Ghent University
摘要：Eliminating the effect of confounding in observational studies typically involves fitting a model for an outcome adjusted for covariates. When, as often, these covariates are high-dimensional, this necessitates the use of sparse estimators, such as the lasso, or other regularization approaches. Naive use of such estimators yields confidence intervals for the conditional treatment effect parameter that are not uniformly valid. Moreover, as the number of covariates grows with the sample size, co...
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作者：Hui, Francis K. C.
作者单位：Australian National University
摘要：Information criteria are commonly used for joint fixed and random effects selection in mixed models. While information criteria are straightforward to implement, a major difficulty in applying them is that they are typically based on maximum likelihood estimates, but calculating such estimates for one candidate mixed model, let alone multiple models, presents a major computational challenge. To overcome this hurdle, we study penalized quasilikelihood estimation and use it as the basis for perf...
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作者：McCullagh, P.; Tresoldi, M. F.
作者单位：University of Chicago
摘要：Quantile matching is a strictly monotone transformation that sends the observed response values to the quantiles of a given target distribution. A profile likelihood-based criterion is developed for comparing one target distribution with another in a linear-model setting.
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作者：He, Xu
作者单位：Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS
摘要：Experimental designs that spread points apart from each other on projections are important for computer experiments, when not necessarily all factors have a substantial influence on the response. We provide a theoretical framework for generating designs that have quasi-optimal separation distance on all the projections and quasi-optimal fill distance on univariate margins. The key is to use special techniques to rotate certain lattices. One such type of design is the class of densest packing-b...
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作者：Sit, T.; Ying, Z.; Yu, Y.
作者单位：Chinese University of Hong Kong; Columbia University; University of Warwick
摘要：Statistical analysis on networks has received growing attention due to demand from various emerging applications. In dynamic networks, one of the key interests is to model the event history of time-stamped interactions among nodes. We model dynamic directed networks via multivariate counting processes. A pseudo partial likelihood approach is exploited to capture the network dependence structure. Asymptotic results are established. Numerical experiments are performed to demonstrate the effectiv...
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作者：Wang, Haiying; Ma, Yanyuan
作者单位：University of Connecticut; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：We investigate optimal subsampling for quantile regression. We derive the asymptotic distribution of a general subsampling estimator and then derive two versions of optimal subsampling probabilities. One version minimizes the trace of the asymptotic variance-covariance matrix for a linearly transformed parameter estimator and the other minimizes that of the original parameter estimator. The former does not depend on the densities of the responses given covariates and is easy to implement. Algo...
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作者：Maruyama, Y.; Strawderman, W. E.
作者单位：Kobe University; Rutgers University System; Rutgers University New Brunswick
摘要：We study admissibility of a subclass of generalized Bayes estimators of a multivariate normal vector in the case where the variance is unknown, under scaled quadratic loss. Minimaxity is established for some of these estimators.
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作者：Chen, Yining
作者单位：University of London; London School Economics & Political Science
摘要：We consider the problem of segmented linear regression with a single breakpoint, with the focus on estimating the location of the breakpoint. If n is the sample size, we show that the global minimax convergence rate for this problem in terms of the mean absolute error is O(n(-1/3)). On the other hand, we demonstrate the construction of a super-efficient estimator that achieves the pointwise convergence rate of either O(n(-1)) or O(n(-1/2)) for every fixed parameter value, depending on whether ...
