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稀疏函数数据均值的平滑同时置信带AI 解读

Functional data analysis (FDA) has become an important area of statistics research in the recent decade, yet a smooth simultaneous confidence corridor (SCC) does not exist in the literature for the mean function of sparse functional data. SCC is a powerful tool for making statistical inference on an entire unknown function, nonetheless classic “Hungarian embedding” techniques for establishing asymptotic correctness of SCC completely fail for sparse functional data. We…

充分降维中中心子空间的融合估计AI 解读

When studying the regression of a univariate variable Y on a vector x of predictors, most existing sufficient dimension-reduction (SDR) methods require the construction of slices of Y to estimate moments of the conditional distribution of X given Y. But there is no widely accepted method for choosing the number of slices, while a poorly chosen slicing scheme may produce miserable results. We propose a novel and easily implemented fusing method that can mitigate the problem…

正则化统计估计的通用路径算法AI 解读

Regularization is widely used in statistics and machine learning to prevent overfitting and gear solution toward prior information. In general, a regularized estimation problem minimizes the sum of a loss function and a penalty term. The penalty term is usually weighted by a tuning parameter and encourages certain constraints on the parameters to be estimated. Particular choices of constraints lead to the popular lasso, fused-lasso, and other generalized ℓ1 penalized…

基于测量误差模型选择似然的非参数分类变量选择AI 解读

Using the relationships among ridge regression, LASSO estimation, and measurement error attenuation as motivation, a new measurement-error-model-based approach to variable selection is developed. After describing the approach in the familiar context of linear regression, we apply it to the problem of variable selection in nonparametric classification, resulting in a new kernel-based classifier with LASSO-like shrinkage and variable-selection properties. Finite-sample…

函数数据的富集折棒过程AI 解读

In many applications involving functional data, prior information is available about the proportion of curves having different attributes. It is not straightforward to include such information in existing procedures for functional data analysis. Generalizing the functional Dirichlet process (FDP), we propose a class of stick-breaking priors for distributions of functions. These priors incorporate functional atoms drawn from constrained stochastic processes. The…

动态基因调控网络建模的稀疏可加常微分方程AI 解读

The gene regulation network (GRN) is a high-dimensional complex system, which can be represented by various mathematical or statistical models. The ordinary differential equation (ODE) model is one of the popular dynamic GRN models. High-dimensional linear ODE models have been proposed to identify GRNs, but with a limitation of the linear regulation effect assumption. In this article, we propose a sparse additive ODE (SA-ODE) model, coupled with ODE estimation methods and…

病例对照数据变系数密度比模型的局部经验似然推断AI 解读

In this article, we develop a varying-coefficient density-ratio model for case-control studies. The case and control samples come from two different distributions. Under the model assumption, the ratio of the two densities is related to the linear combination of covariates with varying coefficients through a known function. A special case is the exponential tilt model where the log ratio of the two densities is a linear function of covariates. We propose a local empirical…

反卷积密度估计中的参数辅助非参数方法AI 解读

Nonparametric estimation of a density from contaminated data is a difficult problem, for which convergence rates are notoriously slow. We introduce parametrically assisted nonparametric estimators which can dramatically improve on the performance of standard nonparametric estimators when the assumed model is close to the true density, without degrading much the quality of purely nonparametric estimators in other cases. We establish optimal convergence rates for our problem…

函数线性模型的自适应整体检验AI 解读

This article studies global testing of the slope function in functional linear regression models. A major challenge in functional global testing is to choose the dimension of projection when approximating the functional regression model by a finite dimensional multivariate linear regression model. We develop a new method that simultaneously tests the slope vectors in a sequence of functional principal components regression models. The sequence of models being tested is…

线性指数族中向量参数的精确方向性推断AI 解读

We consider inference on a vector-valued parameter of interest in a linear exponential family, in the presence of a finite-dimensional nuisance parameter. Based on higher-order asymptotic theory for likelihood, we propose a directional test whose p-value is computed using one-dimensional integration. The work simplifies and develops earlier research on directional tests for continuous models and on higher-order inference for discrete models, and the examples include…

超高维协变量变系数模型的特征选择AI 解读

This paper is concerned with feature screening and variable selection for varying coefficient models with ultrahigh dimensional covariates. We propose a new feature screening procedure for these models based on conditional correlation coefficient. We systematically study the theoretical properties of the proposed procedure, and establish their sure screening property and the ranking consistency. To enhance the finite sample performance of the proposed procedure, we further…

半参数线性模型中的贝叶斯变量选择AI 解读

There is a rich literature on Bayesian variable selection for parametric models. Our focus is on generalizing methods and asymptotic theory established for mixtures of g-priors to semiparametric linear regression models having unknown residual densities. Using a Dirichlet process location mixture for the residual density, we propose a semiparametric g-prior which incorporates an unknown matrix of cluster allocation indicators. For this class of priors, posterior computation…

倾向得分估计的不确定性：贝叶斯变量选择与模型平均因果效应AI 解读

Causal inference with observational data frequently relies on the notion of the propensity score (PS) to adjust treatment comparisons for observed confounding factors. As decisions in the era of "big data" are increasingly reliant on large and complex collections of digital data, researchers are frequently confronted with decisions regarding which of a high-dimensional covariate set to include in the PS model in order to satisfy the assumptions necessary for estimating…

DAE-seq 数据分析：变量选择与观测依赖建模AI 解读

In DAE (DNA After Enrichment)-seq experiments, genomic regions related with certain biological processes are enriched/isolated by an assay and are then sequenced on a high-throughput sequencing platform to determine their genomic positions. Statistical analysis of DAE-seq data aims to detect genomic regions with significant aggregations of isolated DNA fragments ("enriched regions") versus all the other regions ("background"). However, many confounding factors may influence…

观察研究中的整群处理分配与未观测偏差敏感性AI 解读

Clustered treatment assignment occurs when individuals are grouped into clusters prior to treatment and whole clusters, not individuals, are assigned to treatment or control. In randomized trials, clustered assignments may be required because the treatment must be applied to all children in a classroom, or to all patients at a clinic, or to all radio listeners in the same media market. The most common cluster randomized design pairs 2S clusters into S pairs based on similar…

无似然推断的期望传播方法AI 解读

Many models of interest in the natural and social sciences have no closed-form likelihood function, which means that they cannot be treated using the usual techniques of statistical inference. In the case where such models can be efficiently simulated, Bayesian inference is still possible thanks to the approximate Bayesian computation (ABC) algorithm. Although many refinements have been suggested, ABC inference is still far from routine. ABC is often excruciatingly slow due…

化学图谱中核小体定位信号的局部卷积簇模型AI 解读

Nucleosome is the fundamental packing unit of DNA in eukaryotic cells, and its positioning plays a critical role in regulation of gene expression and chromosome functions. Using a recently developed chemical mapping method, nucleosomes can be potentially mapped with an unprecedented single-base-pair resolution. Existence of overlapping nucleosomes due to cell mixture or cell dynamics, however, causes convolution of nucleosome positioning signals. In this paper, we introduce…

利用高频数据估计杠杆效应AI 解读

The leverage effect has become an extensively studied phenomenon that describes the (usually) negative relation between stock returns and their volatility. Although this characteristic of stock returns is well acknowledged, most studies of the phenomenon are based on cross-sectional calibration with parametric models. On the statistical side, most previous works are conducted over daily or longer return horizons, and few of them have carefully studied its estimation,…

利用横截面队列生存数据估计加拿大老年人痴呆终生风险AI 解读

Dementia is one of the world's major public health challenges. The lifetime risk of dementia is the proportion of individuals who ever develop dementia during their lifetime. Despite its importance to epidemiologists and policy-makers, this measure does not seem to have been estimated in the Canadian population. Data from a birth cohort study of dementia are not available. Instead, we must rely on data from the Canadian Study of Heath and Aging, a large cross-sectional study…

《投票权法》第203条语言援助适用范围的统计建模方法AI 解读

Section 203 of the Voting Rights Act includes provisions requiring the use of election materials in languages other than English for states or political subdivisions, specifically, when a minimum number of voting age U.S. citizens of specified language minority groups who are unable to speak English very well and have obtained less than a fifth-grade education is met. Data on these characteristics are provided by the 2010 Census and the American Community Survey (ACS), a…

广义最小二乘矩阵分解AI 解读

Variables in many big-data settings are structured, arising, for example, from measurements on a regular grid as in imaging and time series or from spatial-temporal measurements as in climate studies. Classical multivariate techniques ignore these structural relationships often resulting in poor performance. We propose a generalization of principal components analysis (PCA) that is appropriate for massive datasets with structured variables or known two-way dependencies. By…

通过最小二乘约束估计进行矩阵秩的自助法检验AI 解读

To test if an unknown matrix M0 has a given rank (null hypothesis noted H0), we consider a statistic that is a squared distance between an estimator and the submanifold of fixed-rank matrix. Under H0, this statistic converges to a weighted chi-squared distribution. We introduce the constrained bootstrap (CS bootstrap) to estimate the law of this statistic under H0. An important point is that even if H0 fails, the CS bootstrap reproduces the behavior of the statistic under…

多变量数据多重变点分析的非参数方法AI 解读

Change point analysis has applications in a wide variety of fields. The general problem concerns the inference of a change in distribution for a set of time-ordered observations. Sequential detection is an online version in which new data are continually arriving and are analyzed adaptively. We are concerned with the related, but distinct, offline version, in which retrospective analysis of an entire sequence is performed. For a set of multivariate observations of arbitrary…

稳健多代理古气候重建与温度条件分位数建模AI 解读

Great strides have been made in the field of reconstructing past temperatures based on models relating temperature to temperature-sensitive paleoclimate proxies. One of the goals of such reconstructions is to assess if current climate is anomalous in a millennial context. These regression based approaches model the conditional mean of the temperature distribution as a function of paleoclimate proxies (or vice versa). Some of the recent focus in the area has considered…