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高维主成分分析的一致性与稀疏性AI 解读

Principal components analysis (PCA) is a classic method for the reduction of dimensionality of data in the form of n observations (or cases) of a vector with p variables. Contemporary datasets often have p comparable with or even much larger than n. Our main assertions, in such settings, are (a) that some initial reduction in dimensionality is desirable before applying any PCA-type search for principal modes, and (b) the initial reduction in dimensionality is best achieved…

函数型数据的深度概念AI 解读

The statistical analysis of functional data is a growing need in many research areas. In particular, a robust methodology is important to study curves, which are the output of many experiments in applied statistics. As a starting point for this robust analysis, we propose, analyze, and apply a new definition of depth for functional observations based on the graphic representation of the curves. Given a collection of functions, it establishes the “centrality” of an…

从实验数据推断最优同伴分配AI 解读

This article studies the problem of optimally dividing individuals into peer groups to maximize social gains from heterogeneous peer effects. The specific setting analyzed here concerns efficient ways of allocating roommates in college dormitories. Using confidential data on a sample of Dartmouth College freshmen who were randomly assigned to be roommates, the article derives efficient roommate pairing rules, based on demographic and academic background, which maximize…

带测量误差的部分线性模型变量选择AI 解读

This article focuses on variable selection for partially linear models when the covariates are measured with additive errors. We propose two classes of variable selection procedures, penalized least squares and penalized quantile regression, using the nonconvex penalized principle. The first procedure corrects the bias in the loss function caused by the measurement error by applying the so-called correction-for-attenuation approach, whereas the second procedure corrects the…

最近邻分类的贝叶斯重新评估AI 解读

The k-nearest-neighbor (knn) procedure is a well-known deterministic method used in supervised classification. This article proposes a reassessment of this approach as a statistical technique derived from a proper probabilistic model; in particular, we modify the assessment found in Holmes and Adams, and evaluated by Manocha and Girolami, where the underlying probabilistic model is not completely well defined. Once provided with a clear probabilistic basis for the knn…

用于开发动态治疗方案的筛选实验AI 解读

Dynamic treatment regimes are time-varying treatments that individualize sequences of treatments to the patient. The construction of dynamic treatment regimes is challenging because a patient will be eligible for some treatment components only if he has not responded (or has responded) to other treatment components. In addition there are usually a number of potentially useful treatment components and combinations thereof. In this article, we propose new methodology for…

利用贝叶斯平滑样条混合进行局部谱分析AI 解读

In many practical problems, time series are realizations of nonstationary random processes. These processes can often be modeled as processes with slowly changing dynamics or as piecewise stationary processes. In these cases, various approaches to estimating the time-varying spectral density have been proposed. Our approach in this article is to estimate the log of the Dahlhaus local spectrum using a Bayesian mixture of splines. The basic idea of our approach is to first…

簇相关误差线性模型的秩估计与相关推断AI 解读

R estimators based on the joint ranks (JR) of all the residuals have been developed over the last 20 years for fitting linear models with independently distributed errors. In this article, we extend these estimators to estimating the fixed effects in a linear model with cluster correlated continuous error distributions for general score functions. We discuss the asymptotic theory of the estimators and standard errors of the estimators. For the related mixed model with a…

利用随机小波表示一致分类非平稳时间序列AI 解读

Consider the situation when we have training data containing many time series having known group membership and testing data with unknown group membership. The goals are to find timescale features (using training data) that can best separate the groups, and to use these highly discriminant features to classify test data. We propose a method for classification using a bias-corrected nondecimated wavelet transform. Wavelets are ideal for identifying highly discriminant local…

基于最优预测的稳健响应变量变换AI 解读

Nonlinear regression problems can often be reduced to linearity by transforming the response variable (e.g., using the Box-Cox family of transformations). The classic estimates of the parameter defining the transformation as well as of the regression coefficients are based on the maximum likelihood criterion, assuming homoscedastic normal errors for the transformed response. These estimates are nonrobust in the presence of outliers and can be inconsistent when the errors are…

基于似然的充分降维AI 解读

We obtain the maximum likelihood estimator of the central subspace under conditional normality of the predictors given the response. Analytically and in simulations we found that our new estimator can preform much better than sliced inverse regression, sliced average variance estimation and directional regression, and that it seems quite robust to deviations from normality.

具有半参数因子动态的时间序列建模AI 解读

High-dimensional regression problems, which reveal dynamic behavior, are typically analyzed by time propagation of a few number of factors. The inference on the whole system is then based on the low-dimensional time series analysis. Such high-dimensional problems occur frequently in many different fields of science. In this article we address the problem of inference when the factors and factor loadings are estimated by semiparametric methods. This more flexible modeling…

单倍型病例对照研究中兼顾稳健性与效率的收缩估计AI 解读

Case-control association studies often aim to investigate the role of genes and gene-environment interactions in terms of the underlying haplotypes (i.e., the combinations of alleles at multiple genetic loci along chromosomal regions). The goal of this article is to develop robust but efficient approaches to the estimation of disease odds-ratio parameters associated with haplotypes and haplotype-environment interactions. We consider "shrinkage" estimation techniques that can…

动态平滑系数模型的非参数分位数估计AI 解读

In this article, quantile regression methods are suggested for a class of smooth coefficient time series models. We use both local polynomial and local constant fitting schemes to estimate the smooth coefficients in a quantile framework. We establish the asymptotic properties of both the local polynomial and local constant estimators for α-mixing time series. Also, a bandwidth selector based on the nonparametric version of the Akaike information criterion is suggested,…

大型协方差矩阵的广义阈值化AI 解读

We propose a new class of generalized thresholding operators that combine thresholding with shrinkage, and study generalized thresholding of the sample covariance matrix in high dimensions. Generalized thresholding of the covariance matrix has good theoretical properties and carries almost no computational burden. We obtain an explicit convergence rate in the operator norm that shows the tradeoff between the sparsity of the true model, dimension, and the sample size, and…

利用主分层对职业培训项目因果效应进行似然分析AI 解读

Government-sponsored job-training programs must be subject to evaluation to assess whether their effectiveness justifies their cost to the public. The evaluation usually focuses on employment and total earnings, although the effect on wages is also of interest, because this effect reflects the increase in human capital due to the training program, whereas the effect on total earnings may be simply reflecting the increased likelihood of employment without any effect on wage…

多位点、多种群样本中单核苷酸多态性多样性的贝叶斯层级模型AI 解读

The distribution of genetic variation among populations is conveniently measured by Wright's F(ST), which is a scaled variance taking on values in [0,1]. For certain types of genetic markers, and for single-nucleotide polymorphisms (SNPs) in particular, it is reasonable to presume that allelic differences at most loci are selectively neutral. For such loci, the distribution of genetic variation among populations is determined by the size of local populations, the pattern and…

基因组等容区的贝叶斯分析AI 解读

Statistical identification of isochore structure, the variation in large-scale GC composition (proportion of DNA bases that are G or C as opposed to A or T), of mammalian genomes is a necessary requirement for understanding both the evolution of base composition and the many genomic features such as mutation and recombination rates, which covary with base composition. We have developed a Bayesian method for isochore analysis that we demonstrate to be more accurate than the…

一般科学模型的确定及其资产定价应用AI 解读

We consider a consumption-based asset pricing model that uses habit persistence to overcome the known statistical inadequacies of the classical consumption-based asset pricing model. We find that the habit model fits reasonably well and agrees with results reported in the literature if conditional heteroskedasticity is suppressed but that it does not fit nor do results agree if conditional heteroskedasticity, well known to be present in financial market data, is allowed to…

气候模型集合中不确定性的贝叶斯建模AI 解读

Projections of future climate change caused by increasing greenhouse gases depend critically on numerical climate models coupling the ocean and atmosphere (global climate models [GCMs]). However, different models differ substantially in their projections, which raises the question of how the different models can best be combined into a probability distribution of future climate change. For this analysis, we have collected both current and future projected mean temperatures…

黑质神经元线粒体DNA缺失随机计算模型的贝叶斯仿真代理与校准AI 解读

This article considers the problem of parameter estimation for a stochastic biological model of mitochondrial DNA population dynamics using experimental data on deletion mutation accumulation. The stochastic model is an attempt to describe the hypothesized link between deletion accumulation and neuronal loss in the substantia nigra region of the human brain. Inference for the parameters of the model is complicated by the fact that the model is both analytically intractable…

事件依赖删失与终止下复发事件均值函数及治疗效应的稳健估计AI 解读

In clinical trials featuring recurrent clinical events, the definition and estimation of treatment effects involves a number of interesting issues, especially when loss to follow-up may be event-related and when terminal events such as death preclude the occurrence of further events. This paper discusses a clinical trial of breast cancer patients with bone metastases where the recurrent events are skeletal complications, and where patients may die during the trial. We argue…

函数型预测变量的贝叶斯半参数联合模型AI 解读

Motivated by the need to understand and predict early pregnancy loss using hormonal indicators of pregnancy health, this paper proposes a semiparametric Bayes approach for assessing the relationship between functional predictors and a response. A multivariate adaptive spline model is used to describe the functional predictors, and a generalized linear model with a random intercept describes the response. Through specifying the random intercept to follow a Dirichlet process…

GARCH 系数为零的检验：标准检验修正与相对效率比较AI 解读

This article is concerned with testing the nullity of coefficients in generalized autoregressive conditionally heteroscedastic (GARCH) models. The problem is nonstandard because the quasi-maximum likelihood estimator is subject to positivity constraints. This article establishes the asymptotic null and local alternative distributions of Wald, score, and quasi-likelihood ratio tests. Efficiency comparisons under fixed alternatives are considered. Two cases of special interest…