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二元结果半参数样本选择模型的边际效应估计AI 解读

This paper addresses the estimation of a semiparametric sample selection index model where both the selection rule and the outcome variable are binary. Since the marginal effects are often of primary interest and are difficult to recover in a semiparametric setting, we focus on developing an estimator for the marginal effects. This marginal effect estimator uses only observations where the selection probability is above a certain threshold. A key innovation is that this high…

区间数据下半参数二元响应模型的推断AI 解读

This paper studies the semiparametric binary response model with interval data investigated by Manski and Tamer (2002). In this partially identified model, we propose a new estimator based on MT’s modified maximum score (MMS) method by introducing density weights to the objective function, which allows us to develop asymptotic properties of the proposed set estimator for inference. We show that the density-weighted MMS estimator converges at a nearly cube-root-n rate. We…

含样本选择的乘法误差模型AI 解读

This paper presents a simple approach to deal with sample selection in models with multiplicative errors. Models for non-negative limited dependent variables such as counts fit this framework. The approach builds on a specification of the conditional mean of the outcome only and is, therefore, semiparametric in nature. GMM estimators are constructed for both cross-section data and for panel data. We derive distribution theory and present Monte Carlo evidence on the…

零膨胀与过度离散计数数据的位置、尺度和形状贝叶斯广义加性模型AI 解读

Frequent problems in applied research preventing the application of the classical Poisson log-linear model for analyzing count data include overdispersion, an excess of zeros compared to the Poisson distribution, correlated responses, as well as complex predictor structures comprising nonlinear effects of continuous covariates, interactions or spatial effects. We propose a general class of Bayesian generalized additive models for zero-inflated and overdispersed count data…

非对称独立成分分析的 R 估计AI 解读

Independent component analysis (ICA) recently has attracted much attention in the statistical literature as an appealing alternative to elliptical models. Whereas k-dimensional elliptical densities depend on one single unspecified radial density, however, k-dimensional independent component distributions involve k unspecified component densities. In practice, for given sample size n and dimension k, this makes the statistical analysis much harder. We focus here on the…

变指标系数模型AI 解读

It has been a long history of using interactions in regression analysis to investigate alterations in covariate-effects on response variables. In this article, we aim to address two kinds of new challenges arising from the inclusion of such high-order effects in the regression model for complex data. The first kind concerns a situation where interaction effects of individual covariates are weak but those of combined covariates are strong, and the other kind pertains to the…

传染病传播数据的半参数相对风险回归AI 解读

This article introduces semiparametric relative-risk regression models for infectious disease data. The units of analysis in these models are pairs of individuals at risk of transmission. The hazard of infectious contact from i to j consists of a baseline hazard multiplied by a relative risk function that can be a function of infectiousness covariates for i, susceptibliity covariates for j, and pairwise covariates. When who-infects-whom is observed, we derive a profile…

Copula 模型设定的光滑检验AI 解读

We present a family of smooth tests for the goodness of fit of semiparametric multivariate copula models. The proposed tests are distribution free and can be easily implemented. They are diagnostic and constructive in the sense that when a null distribution is rejected, the test provides useful pointers to alternative copula distributions. We then propose a method of copula density construction, which can be viewed as a multivariate extension of Efron and Tibshirani. We…

丰富数据环境下经济变量的分布预测AI 解读

This article investigates the relevance of considering a large number of macroeconomic indicators to forecast the complete distribution of a variable. The baseline time series model is a semiparametric specification based on the quantile autoregressive (QAR) model that assumes that the quantiles depend on the lagged values of the variable. We then augment the time series model with macroeconomic information from a large dataset by including principal components or a subset…

允许未观测变量受异质影响的半参数单指标模型AI 解读

This paper proposes a single-index semiparametric model in which the unknown function has cross-sectional unit specific weights. The initial motivation comes from the search for a better measure of liquidity in stock trading which is captured by the unknown function here. The model is estimated by semiparametric least squares developed by Ichimura (1993) and Ichimura and Lee (1991). The proposed technique differs from theirs in at least two aspects. First, I show that the…

纳入基线协变量的半参数自然直接效应模型估计AI 解读

Establishing cause-effect relationships is a standard goal of empirical science. Once the presence of a causal relationship is established, the precise causal mechanism involved becomes a topic of interest. A particularly popular type of mechanism analysis concerns questions of mediation, that is to what extent an effect is direct, and to what extent it is mediated by a third variable. A semiparametric theory has recently been proposed which allows multiply robust estimation…

利用失败联系记录校正不可忽略无应答的半参数极大似然推断AI 解读

In marketing research, social science and epidemiological studies, call-back of nonrespondents is standard. If respondents and nonrespondents tend to give different answers, the missing data are called nonignorable, and using them alone may produce biased results. To extend earlier work on nonresponse in the presence of call-backs, Alho (1990) proposed modelling the probability of response at each attempt through logistic regression, where outcomes of interest and covariates…

密度加权平均导数的 Bootstrap 推断AI 解读

We investigate the properties of several bootstrap-based inference procedures for semiparametric density-weighted average derivatives. The key innovation in this paper is to employ an alternative asymptotic framework to assess the properties of these inference procedures. This theoretical approach is conceptually distinct from the traditional approach (based on asymptotic linearity of the estimator and Edgeworth expansions), and leads to different theoretical prescriptions…

条件平均处理效应的估计AI 解读

We consider a functional parameter called the conditional average treatment effect (CATE), designed to capture heterogeneity of a treatment effect across subpopulations when the unconfoundedness assumption applies. In contrast to quantile regressions, the subpopulations of interest are defined in terms of the possible values of a set of continuous covariates rather than the quantiles of the potential outcome distributions. We show that the CATE parameter is nonparametrically…

中心均值子空间的估计效率AI 解读

Summary We investigate the estimation efficiency of the central mean subspace in the framework of sufficient dimension reduction. We derive the semiparametric efficient score and study its practical applicability. Despite the difficulty caused by the potential high dimension issue in the variance component, we show that locally efficient estimators can be constructed in practice. We conduct simulation studies and a real data analysis to demonstrate the finite sample…

加速失效时间模型中基于嵌入似然的得分估计方程AI 解读

The semiparametric accelerated failure time (AFT) model is one of the most popular models for analyzing time-to-event outcomes. One appealing feature of the AFT model is that the observed failure time data can be transformed to identically independent distributed random variables without covariate effects. We describe a class of estimating equations based on the score functions for the transformed data, which are derived from the full likelihood function under commonly used…

稀疏半参数非线性模型及其色谱指纹应用AI 解读

Traditional Chinese herbal medications (TCHMs) are comprised of a multitude of compounds and the identification of their active composition is an important area of research. Chromatography provides a visual representation of a TCHM sample's composition by outputting a curve characterized by spikes corresponding to compounds in the sample. Across different experimental conditions, the location of the spikes can be shifted, preventing direct comparison of curves and forcing…

半参数高斯 Copula 模型：几何与有效秩估计AI 解读

We propose, for multivariate Gaussian copula models with unknown margins and structured correlation matrices, a rank-based, semiparametrically efficient estimator for the Euclidean copula parameter. This estimator is defined as a one-step update of a rank-based pilot estimator in the direction of the efficient influence function, which is calculated explicitly. Moreover, finite-dimensional algebraic conditions are given that completely characterize efficiency of the…

超高维纵向数据的非参数独立筛选与结构识别AI 解读

Ultra-high dimensional longitudinal data are increasingly common and the analysis is challenging both theoretically and methodologically. We offer a new automatic procedure for finding a sparse semivarying coefficient model, which is widely accepted for longitudinal data analysis. Our proposed method first reduces the number of covariates to a moderate order by employing a screening procedure, and then identifies both the varying and constant coefficients using a group SCAD…

低频及其他污染下长记忆过程的修正局部 Whittle 估计AI 解读

We propose a modified local-Whittle estimator of the memory parameter of a long memory time series process which has good properties under an almost complete collection of contamination processes that have been discussed in the literature, mostly separately. These contaminations include processes whose spectral density functions dominate at low frequencies such as random level shifts, deterministic level shifts and deterministic trends. We show that our modified estimator…

私人信号相关的不完全信息二元决策博弈的半参数识别AI 解读

This paper studies the identification and estimation of a static binary decision game of incomplete information. We make no parametric assumptions on the joint distribution of private signals and allow them to be correlated. We show that the parameters of interest can be point-identified subject to a scale normalization under mild support requirements for the regressors (publicly observed state variables) and errors (private signals). Following Manski and Tamer (2002), we…

含共同因子的大型动态面板模型的估计效率AI 解读

This paper deals with asymptotically efficient estimation in exchangeable nonlinear dynamic panel models with common unobservable factors. These models are relevant for applications to large portfolios of credits, corporate bonds, or life insurance contracts. For instance, the Asymptotic Risk Factor (ARF) model is recommended in the current regulation in Finance (Basel II and Basel III) and Insurance (Solvency II) for risk prediction and computation of the required capital.…

单变量与多变量群组检测数据的非参数及半参数回归新方法AI 解读

We consider nonparametric and semiparametric estimation of a conditional probability curve in the case of group testing data, where the individuals are pooled randomly into groups and only the pooled data are available. We derive a nonparametric weighted estimator that has optimality properties accounting for group sizes, and show how to extend it to multivariate settings, including the partially linear model. In the group testing context, it is natural to assume that the…

半参数混样检测回归模型AI 解读

Group testing, through the use of pooling, has proven to be an efficient method of reducing the time and cost associated with screening for a binary characteristic of interest, such as infection status. A topic of key interest in the statistical literature involves the development of regression models that relate individual-level covariates to testing responses observed from pooled specimens. In this article, we propose a general semiparametric framework that allows for the…