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SPARCC：右删失协变量模型中的半参数稳健估计AI 解读

In Huntington disease research, a current goal is to understand how symptoms change prior to a clinical diagnosis. Statistically, achieving this goal entails modeling symptom severity as a function of the covariate 'time of diagnosis,' which is often heavily right-censored in observational studies. Existing estimators that handle right-censored covariates, such as the complete case estimator and maximum likelihood estimator, vary in their statistical efficiency and…

边际均值效应与边际分位数效应估计的半参数方法AI 解读

We consider a semiparametric generalized linear model and study estimation of both marginal mean effects and marginal quantile effects in this model. We propose an approximate maximum likelihood estimator, and rigorously establish the consistency, the asymptotic normality, and the semiparametric efficiency of our method in both the marginal mean effect and the marginal quantile effect estimation. Simulation studies are conducted to illustrate the finite sample performance,…

标签分布迁移下的双重灵活估计AI 解读

In studies ranging from clinical medicine to policy research, complete data are usually available from a population P, but the quantity of interest is often sought for a related but different population Q which only has partial data. We consider the setting when both outcome Y and covariate X are available from P but only X is available from Q, under the label shift assumption; that is, the conditional distribution of X given Y is the same in the two populations. To estimate…

用于稳健共同基金筛选的单侧精细对称化数据聚合方法AI 解读

We consider the problem of identifying skilled funds among a large number of candidates under the linear factor pricing models containing both observable and latent market factors. Motivated by the existence of non-strong potential factors and diversity of error distribution types of the linear factor pricing models, we develop a distribution-free multiple testing procedure to solve this problem. The proposed procedure is established based on the statistical tool of…

利用可能部分共享参数的多样本模型进行预测AI 解读

We consider prediction based on a main model. When the main model shares partial parameters with several other helper models, we make use of the additional information. Specifically, we propose a Model Averaging Prediction (MAP) procedure that takes into account data related to the main model as well as data related to the helper models. We allow the data related to different models to follow different structures, as long as they share some common covariate effect. We show…

高维随机向量相互独立性的秩最大值—求和检验AI 解读

We consider the problem of testing mutual independence of high-dimensional random vectors, and propose a series of high-dimensional rank-based max-sum tests, which are suitable for high-dimensional data and can be robust to distribution types of the variables, form of the dependence between variables and the sparsity of correlation coefficients. Further, we demonstrate the application of some representative members of the proposed tests on testing cross-sectional…

含测量误差的高维泊松模型：非线性非凸优化中的假设检验AI 解读

We study estimation and testing in the Poisson regression model with noisy high-dimensional covariates, which has wide applications in analyzing noisy big data. Correcting for the estimation bias due to the covariate noise leads to a nonconvex target function to minimize. Treating the high-dimensional issue further leads us to augment an amenable penalty term to the target function. We propose to estimate the regression parameter through minimizing the penalized target…

网络函数型变系数模型AI 解读

We consider functional responses with network dependence observed for each individual at irregular time points. To model both the interindividual dependence and within-individual dynamic correlation, we propose a network functional varying coefficient (NFVC) model. The response of each individual is characterized by a linear combination of responses from its connected nodes and its exogenous covariates. All the model coefficients are allowed to be time dependent. The NFVC…

无需估计非可忽略缺失机制的通用估计程序AI 解读

We consider the estimation problem in a regression setting where the outcome variable is subject to nonignorable missingness and identifiability is ensured by the shadow variable approach. We propose a versatile estimation procedure where modeling of missingness mechanism is completely bypassed. We show that our estimator is easy to implement and we derive the asymptotic theory of the proposed estimator. We also investigate some alternative estimators under different…

稀疏备择下线性因子定价模型 Alpha 的高维检验AI 解读

We consider the problem of testing for the presence of alpha in Linear Factor Pricing Models. We propose a novel test of the max-of-squares type, which is designed to deal with the high dimensionality of the securities and the sparse alternatives. We rigorously show that the proposed test has attractive theoretical properties and demonstrate its superior performance via Monte Carlo experiments. These results are established when the number of securities is larger than the…

高维数据位置参数的逆范数符号检验AI 解读

We consider the one sample location testing problem for high-dimensional data, where the data dimension is potentially much larger than the sample size. We devise a novel inverse norm sign test (INST) that is consistent and has much improved power than many existing popular tests. We further construct a general class of weighted spatial sign tests which includes these existing tests, and show that INST is the optimal member within this class, in that it is consistent and is…

Summary In this paper, we develop a model averaging method to estimate a high-dimensional covariance matrix, where the candidate models are constructed by different orders of polynomial functions. We propose a Mallows-type model averaging criterion and select the weights by minimizing this criterion, which is an unbiased estimator of the expected in-sample squared error plus a constant. Then, we prove the asymptotic optimality of the resulting model average covariance…

大数据分位数回归的最优子抽样AI 解读

Summary 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…

MALMEM：线性测量误差模型中的模型平均AI 解读

Summary We develop model averaging estimation in the linear regression model where some covariates are subject to measurement error. The absence of the true covariates in this framework makes the calculation of the standard residual-based loss function impossible. We take advantage of the explicit form of the parameter estimators and construct a weight choice criterion. It is asymptotically equivalent to the unknown model average estimator minimizing the loss function. When…

复杂模型的基于模拟的偏差校正方法AI 解读

Along with the ever increasing data size and model complexity, an important challenge frequently encountered in constructing new estimators or in implementing a classical one such as the maximum likelihood estimator, is the computational aspect of the estimation procedure. To carry out estimation, approximate methods such as pseudo-likelihood functions or approximated estimating equations are increasingly used in practice as these methods are typically easier to implement…

通过函数型线性回归模型平均进行函数预测AI 解读

Prediction is often the primary goal of data analysis. In this work, we propose a novel model averaging approach to the prediction of a functional response variable. We develop a crossvalidation model averaging estimator based on functional linear regression models in which the response and the covariate are both treated as random functions. We show that the weights chosen by the method are asymptotically optimal in the sense that the squared error loss of the predicted…

病例对照研究次要结局分位数回归的半参数有效估计AI 解读

Summary Analysing secondary outcomes is a common practice for case–control studies. Traditional secondary analysis employs either completely parametric models or conditional mean regression models to link the secondary outcome to covariates. In many situations, quantile regression models complement mean-based analyses and provide alternative new insights on the associations of interest. For example, biomedical outcomes are often highly asymmetric, and median regression is…

不可忽略无应答下多元缺失数据分析的最优伪似然估计AI 解读

Tang et al. (2003) considered a regression model with missing response, where the missingness mechanism depends on the value of the response variable and hence is nonignorable. They proposed three pseudolikelihood estimators, based on different treatments of the probability distribution of the completely observed covariates. The first assumes the distribution of the covariate to be known, the second estimates this distribution parametrically, and the third estimates the…

病例对照研究中复杂多基因与环境交互作用的半参数分析AI 解读

Many methods have recently been proposed for efficient analysis of case-control studies of gene-environment interactions using a retrospective likelihood framework that exploits the natural assumption of gene-environment independence in the underlying population. However, for polygenic modelling of gene-environment interactions, which is a topic of increasing scientific interest, applications of retrospective methods have been limited due to a requirement in the literature…

跨人群的半参数单指数风险评分AI 解读

We consider a problem motivated by issues in nutritional epidemiology, across diseases and populations. In this area, it is becoming increasingly common for diseases to be modeled by a single diet score, such as the Healthy Eating Index, the Mediterranean Diet Score, etc. For each disease and for each population, a partially linear single-index model is fit. The partially linear aspect of the problem is allowed to differ in each population and disease. However, and…

协变量含测量误差时的线性模型选择AI 解读

Prediction precision is arguably the most relevant criterion of a model in practice and is often a sought after property. A common difficulty with covariates measured with errors is the impossibility of performing prediction evaluation on the data even if a model is completely given without any unknown parameters. We bypass this inherent difficulty by using special properties on moment relations in linear regression models with measurement errors. The end product is a model…

受限矩模型中未知异方差误差分布与协变量误测的联合处理AI 解读

We develop consistent and efficient estimation of parameters in general regression models with mismeasured covariates. We assume the model error and covariate distributions are unspecified, and the measurement error distribution is a general parametric distribution with unknown variance–covariance. We construct root-n consistent, asymptotically normal and locally efficient estimators using the semiparametric efficient score. We do not estimate any unknown distribution or…

存在删失与协变量测量误差的比例优势模型分析AI 解读

We propose a consistent method for estimating both the finite- and infinite-dimensional parameters of the proportional odds model when a covariate is subject to measurement error and time-to-events are subject to right censoring. The proposed method does not rely on the distributional assumption of the true covariate, which is not observed in the data. In addition, the proposed estimator does not require the measurement error to be normally distributed or to have any other…

病例对照研究二次分析中的半参数估计AI 解读

Summary We study the regression relationship between covariates in case–control data: an area known as the secondary analysis of case–control studies. The context is such that only the form of the regression mean is specified, so that we allow an arbitrary regression error distribution, which can depend on the covariates and thus can be heteroscedastic. Under mild regularity conditions we establish the theoretical identifiability of such models. Previous work in this context…