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连续响应观测驱动时间序列模型的信息论最优性AI 解读

We investigate information-theoretic optimality properties of the score function of the predictive likelihood as a device for updating a real-valued time-varying parameter in a univariate observation-driven model with continuous responses. We restrict our attention to models with updates of one lag order. The results provide theoretical justification for a class of score-driven models which includes the generalized autoregressive conditional heteroskedasticity model as a…

加性模型的自动结构恢复AI 解读

We propose an automatic structure recovery method for additive models, based on a backfitting algorithm coupled with local polynomial smoothing, in conjunction with a new kernel-based variable selection strategy. Our method produces estimates of the set of noise predictors, the sets of predictors that contribute polynomially at different degrees up to a specified degree M, and the set of predictors that contribute beyond polynomially of degree M. We prove consistency of the…

重尾数据的半参数指数族方法AI 解读

We propose a semiparametric method for fitting the tail of a heavy-tailed population given a relatively small sample from that population and a larger sample from a related background population. We model the tail of the small sample as an exponential tilt of the better-observed large-sample tail, using a robust sufficient statistic motivated by extreme value theory. In particular, our method induces an estimator of the small-population mean, and we give theoretical and…

使用无偏似然估计时的 MCMC 高效实现AI 解读

When an unbiased estimator of the likelihood is used within a Metropolis–Hastings chain, it is necessary to trade off the number of Monte Carlo samples used to construct this estimator against the asymptotic variances of the averages computed under this chain. Using many Monte Carlo samples will typically result in Metropolis–Hastings averages with lower asymptotic variances than the corresponding averages that use fewer samples; however, the computing time required to…

协变量存在时两种异方差处理的最优设计AI 解读

Optimum designs are described for two treatments with different variances when covariates are included in the model. The designs, a generalization of Neyman allocation, are required in personalized medicine to model the effect of covariates on the choice of treatment. The use of the designs in clinical trials is indicated. D-optimality of the designs is established using results from Kiefer’s general equivalence theorem. The results are obtained with the use of surprisingly…

差异网络检验及其在基因交互识别中的应用AI 解读

Model organisms and human studies have yielded increasing empirical evidence that interactions among genes contribute broadly to genetic variation of complex traits. In the presence of gene-gene interactions, the dimensionality of the feature space becomes extremely high relative to the sample size. This poses a significant methodological challenge in the identification of gene-gene interactions. In this paper, by using a Gaussian graphical model framework, we translate the…

多元回归降秩估计量的有效自由度AI 解读

We study the effective degrees of freedom of a general class of reduced-rank estimators for multivariate regression in the framework of Stein's unbiased risk estimation. A finite-sample exact unbiased estimator is derived that admits a closed-form expression in terms of the thresholded singular values of the least-squares solution and hence is readily computable. The results continue to hold in the high-dimensional setting where both the predictor and the response dimensions…

超高维异质数据的条件分位数筛选AI 解读

To accommodate the heterogeneity that is often present in ultrahigh-dimensional data, we propose a conditional quantile screening method, which enables us to select features that contribute to the conditional quantile of the response given the covariates. The method can naturally handle censored data by incorporating a weighting scheme through redistribution of the mass to the right; moreover, it is invariant to monotone transformation of the response and requires…

多变量最大稳定空间过程AI 解读

Max-stable processes allow the spatial dependence of extremes to be modelled and quantified, so they are widely adopted in applications. For a better understanding of extremes, it may be useful to study several variables simultaneously. To this end, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. We define a Poisson process construction and introduce multivariate versions of the Smith Gaussian extreme-value,…

含纵向协变量的扩展风险率模型AI 解读

In clinical trials and other medical studies, it has become increasingly common to observe simultaneously an event time of interest and longitudinal covariates. In the literature, joint modelling approaches have been employed to analyse both survival and longitudinal processes and to investigate their association. However, these approaches focus mostly on developing adaptive and flexible longitudinal processes based on a prespecified survival model, most commonly the Cox…

信息性响应缺失下线性混合效应模型的变换方法AI 解读

We consider a linear mixed-effects model in which the response panel vector has missing components and the missing data mechanism depends on observed data as well as missing responses through unobserved random effects. Using a transformation of the data that eliminates the random effects, we derive asymptotically unbiased and normally distributed estimators of certain model parameters. Estimators of model parameters that cannot be estimated using the transformed data are…

无限维数据的 Wilcoxon–Mann–Whitney 型检验AI 解读

The Wilcoxon–Mann–Whitney test is a robust competitor of the |$t$| test in the univariate setting. For finite-dimensional multivariate non-Gaussian data, several extensions of the Wilcoxon–Mann–Whitney test have been shown to outperform Hotelling's |$T^{2}$| test. In this paper, we study a Wilcoxon–Mann–Whitney-type test based on spatial ranks in infinite-dimensional spaces, we investigate its asymptotic properties and compare it with several existing tests. The proposed…

每个外层样本仅用一次内层模拟的双重 Bootstrap 方法AI 解读

We show that, when the double bootstrap is used to improve performance of bootstrap methods for bias correction, techniques based on using a single double-bootstrap sample for each single-bootstrap sample can produce third-order accuracy for much less computational expense than is required by conventional double-bootstrap methods. However, this improved level of performance is not available for the single double-bootstrap methods that have been suggested to construct…

基于 Hellinger 积分的降维方法AI 解读

Sufficient dimension reduction is a useful tool for studying the dependence between a response and a multi-dimensional predictor. In this article, a new formulation is proposed that is based on the Hellinger integral of order two, introduced as a natural measure of the regression information contained in the predictor subspace. The response may be either continuous or discrete. We establish links between local and global central subspaces, and propose an efficient local…

最小绝对偏差回归及其他 Z 估计问题的一致有效选择后推断AI 解读

We develop uniformly valid confidence regions for regression coefficients in a high-dimensional sparse median regression model with homoscedastic errors. Our methods are based on a moment equation that is immunized against nonregular estimation of the nuisance part of the median regression function by using Neyman’s orthogonalization. We establish that the resulting instrumental median regression estimator of a target regression coefficient is asymptotically normally…

函数型数据的变系数加性模型AI 解读

Both varying-coefficient and additive models have been studied extensively in the literature as extensions to linear models. They have also been extended to deal with functional response data. However, existing extensions are still not flexible enough to reflect the functional nature of the responses. In this paper, we extend varying-coefficient and additive models to obtain a much more flexible model and propose a simple algorithm to estimate its nonparametric additive and…

混合图模型的选择与估计AI 解读

We consider the problem of estimating the parameters in a pairwise graphical model in which the distribution of each node, conditioned on the others, may have a different exponential family form. We identify restrictions on the parameter space required for the existence of a well-defined joint density, and establish the consistency of the neighbourhood selection approach for graph reconstruction in high dimensions when the true underlying graph is sparse. Motivated by our…

删失数据下个体化治疗规则估计的双重稳健学习AI 解读

Individualized treatment rules recommend treatments based on individual patient characteristics in order to maximize clinical benefit. When the clinical outcome of interest is survival time, estimation is often complicated by censoring. We develop nonparametric methods for estimating an optimal individualized treatment rule in the presence of censored data. To adjust for censoring, we propose a doubly robust estimator which requires correct specification of either the…

纵向数据比例似然比模型的矩型估计量AI 解读

Luo & Tsai, Biometrika99, 211–22, 2012, proposed a proportional likelihood ratio model and discussed a maximum likelihood method for its parameter estimation. In this paper, we use this model as the marginal distribution to analyse longitudinal data, where the maximum likelihood method is not directly applicable because the joint distribution is not fully specified. We propose a moment-type method that is an extension of the generalized estimating equation method. The…

易处理且可解释的四参数圆周单峰分布族AI 解读

This article presents a class of four-parameter distributions for circular data that are unimodal, possess simple characteristic and density functions and a tractable distribution function, can be interpretably parameterized directly in terms of their trigonometric moments, afford a very wide range of skewness and kurtosis, envelop numerous interesting submodels including the wrapped Cauchy and cardioid distributions, allow straightforward parameter estimation by both method…

时间扭曲函数型回归AI 解读

A characteristic feature of functional data is the presence of phase variability in addition to amplitude variability. Existing functional regression methods do not handle time variability in an explicit and efficient way. In this paper we introduce a functional regression method that incorporates time warping as an intrinsic part of the model. The method achieves good predictive power in a parsimonious way and allows unified statistical inference about phase and amplitude…

协方差增强判别分析AI 解读

Linear discriminant analysis has been widely used to characterize or separate multiple classes via linear combinations of features. However, the high dimensionality of features from modern biological experiments defies traditional discriminant analysis techniques. Possible interfeature correlations present additional challenges and are often underused in modelling. In this paper, by incorporating possible interfeature correlations, we propose a covariance-enhanced…

用于贝叶斯聚类的广义Ewens–Pitman模型AI 解读

We propose a Bayesian method for clustering from discrete data structures that commonly arise in genetics and other applications. This method is equivariant with respect to relabelling units; unsampled units do not interfere with sampled data; and missing data do not hinder inference. Cluster inference using the posterior mode performs well on simulated and real datasets, and the posterior predictive distribution enables supervised learning based on a partial clustering of…

利用协变量特定患病率信息提高病例对照研究功效AI 解读

Public registration databases and large cohort studies provide vital information on disease prevalence at various levels of a risk factor. This auxiliary information can be helpful in conducting statistical inference in a new study. We aim to develop a statistical procedure that improves the efficiency of the logistic regression model for a case-control study by utilizing auxiliary information on covariate-specific disease prevalence via a series of unbiased estimating…