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半参数优势比模型的似然推断AI 解读

A flexible semiparametric odds ratio model has been proposed to unify and to extend both the log-linear model and the joint normal model for data with a mix of discrete and continuous variables. The semiparametric odds ratio model is particularly useful for analyzing biased sampling designs. However, statistical inference of the model has not been systematically studied when more than one nonparametric component is involved in the model. In this article, we study the maximum…

多来源数据的半参数贝叶斯密度估计：全球儿童营养不足荟萃分析AI 解读

Undernutrition, resulting in restricted growth, and quantified here using height-for-age z-scores, is an important contributor to childhood morbidity and mortality. Since all levels of mild, moderate, and severe undernutrition are of clinical and public health importance, it is of interest to estimate the shape of the z-scores’ distributions. We present a finite normal mixture model that uses data on 4.3 million children to make annual country-specific estimates of these…

股权风险溢价预测：全局历史平均、局部历史平均与约束AI 解读

The equity premium, return on equity minus return on risk-free asset, is expected to be positive. We consider imposing such positivity constraint in local historical average (LHA) in nonparametric kernel regression framework. It is also extended to the semiparametric single index model when multiple predictors are used. We construct the constrained LHA estimator via an indicator function which operates as “model-selection” between the unconstrained LHA and the bound of the…

一种灵活的半参数时间序列预测模型AI 解读

In this paper, we propose a semiparametric procedure called the “Model Averaging MArginal Regression” (MAMAR) that is flexible for forecasting of time series. This procedure considers approximating a multivariate regression function by an affine combination of one-dimensional marginal regression functions. The weight parameters involved in the approximation are estimated by least squares on the basis of the first-stage nonparametric kernel estimates of the marginal…

时变参数回归模型的半参数建模AI 解读

This paper considers the problem of semiparametric model building for linear regression models with potentially time-varying coefficients. By allowing the response variable and explanatory variables be jointly a nonstationary process, the proposed methods are widely applicable to nonstationary and dependent observations. We propose a local linear shrinkage method that can simultaneously achieve parameter estimation and variable selection. Its selection consistency and the…

多水平干预与多分量中介下的因果中介分析AI 解读

Summary Mediation analysis is an important tool in social and medical sciences as it helps to understand why an intervention works. The commonly used approach, given by Baron and Kenny, requires the strong assumption ‘sequential ignorability’ to yield causal interpretation. Ten Have and his colleagues proposed a rank preserving model to relax this assumption. However, the rank preserving model is restricted to the case with binary intervention and single mediator and needs…

二元响应模型中基于准则函数的集合估计量的非标准收敛速度AI 解读

This paper establishes consistency and non‐standard rates of convergence for set estimators based on contour sets of criterion functions for a semi‐parametric binary response model under a conditional median restriction. The model can be partially identified due to potentially limited‐support regressors and an unknown distribution of errors. A set estimator analogous to the maximum score estimator is essentially cube‐root consistent for the identified set when a continuous…

多工具变量下的正则化有限信息最大似然估计AI 解读

The use of many moment conditions improves the asymptotic efficiency of the instrumental variables estimators. However, in finite samples, the inclusion of an excessive number of moments increases the bias. To solve this problem, we propose regularized versions of the limited information maximum likelihood (LIML) based on three different regularizations: Tikhonov, Landweber–Fridman, and principal components. Our estimators are consistent and asymptotically normal under…

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…

定价核估计：局部估计方程方法AI 解读

This paper investigates a general semiparametric stochastic discount factor formulation that avoids functional form misspecification. A new semiparametric estimation procedure is proposed which combines orthogonality conditions and local linear fitting to give a semiparametric generalized estimating equation approach. Asymptotic properties of the estimators are established and we explore the empirical usefulness of the proposed approach to value-weighted stock returns.

基于回归的协整系统分析AI 解读

Two estimation procedures dominate the cointegration literature: Johansen’s maximum likelihood inference on vector autoregressive error correction models and estimation of Phillips’ triangular forms. This latter methodology is essentially semiparametric, focusing on estimating long run parameters by means of cointegrating regressions. However, it is less used in practice than Johansen’s approach, since its implementation requires prior knowledge of features such as the…

基于期权面板的参数推断与动态状态恢复AI 解读

We develop a new parametric estimation procedure for option panels observed with error. We exploit asymptotic approximations assuming an ever increasing set of option prices in the moneyness (cross-sectional) dimension, but with a fixed time span. We develop consistent estimators for the parameters and the dynamic realization of the state vector governing the option price dynamics. The estimators converge stably to a mixed-Gaussian law and we develop feasible estimators for…

含空间相关随机效应的火炬松生存分析AI 解读

Loblolly pine, a native pine species of the southeastern United States, is the most-planted species for commercial timber. Predicting survival of loblolly pine following planting is of great interest to researchers in forestry science as it is closely related to the yield of timber. Data were collected from a region-wide thinning study, where permanent plots, located at 182 sites ranging from central Texas east to Florida and north to Delaware, were established in 1980–1981.…

存在缺失观测时的有效分位数回归分析AI 解读

This article examines the problem of estimation in a quantile regression model when observations are missing at random under independent and nonidentically distributed errors. We consider three approaches of handling this problem based on nonparametric inverse probability weighting, estimating equations projection, and a combination of both. An important distinguishing feature of our methods is their ability to handle missing response and/or partially missing covariates,…

分层抽样下聚类失效时间的半参数加速失效时间建模AI 解读

Clustered failure times often arise from studies with stratified sampling designs where it is desired to reduce both cost and sampling error. Semiparametric accelerated failure time (AFT) models have not been used as frequently as Cox relative risk models in such settings due to lack of efficient and reliable computing routines for inferences. The challenge roots in the nonsmoothness of the rank-based estimating functions, and for clustered data, the asymptotic properties of…

基于 Copula 多元模型的半参数条件分位数估计AI 解读

We consider a new approach in quantile regression modeling based on the copula function that defines the dependence structure between the variables of interest. The key idea of this approach is to rewrite the characterization of a regression quantile in terms of a copula and marginal distributions. After the copula and the marginal distributions are estimated, the new estimator is obtained as the weighted quantile of the response variable in the model. The proposed…

Let $(X_{1},Y_{1}),\ldots,(X_{n},Y_{n})$ be an i.i.d. sample from a bivariate distribution function that lies in the max-domain of attraction of an extreme value distribution. The asymptotic joint distribution of the standardized component-wise maxima $\bigvee_{i=1}^{n}X_{i}$ and $\bigvee_{i=1}^{n}Y_{i}$ is then characterized by the marginal extreme value indices and the tail copula $R$. We propose a procedure for constructing asymptotically distribution-free goodness-of-fit…

具有非参数异方差的贝叶斯回归AI 解读

This paper studies large sample properties of a semiparametric Bayesian approach to inference in a linear regression model. The approach is to model the distribution of the regression error term by a normal distribution with the variance that is a flexible function of covariates. The main result of the paper is a semiparametric Bernstein–von Mises theorem under misspecification: even when the distribution of the regression error term is not normal, the posterior distribution…

非参数与半参数固定效应面板模型的渐近理论AI 解读

In this paper, we investigate the problem of estimating nonparametric and semiparametric panel data models with fixed effects. We focus on establishing the asymptotic results for estimators using smooth backfitting methods. We consider two estimators for the smooth unknown function in nonparametric panel regressions. One is a local linear estimator constructed similar as the one in Mammen et al. (2009) which was proposed for the additive nonparametric panel model. The other…

Abstract This paper generalizes existing econometric models for censored competing risks by introducing a new flexible specification based on a piecewise linear baseline hazard, time‐varying regressors, and unobserved individual heterogeneity distributed as an infinite mixture of generalized inverse Gaussian (GIG) densities, nesting the gamma kernel as a special case. A common correlated latent time effect induces dependence among risks. Our model is based on underlying…

总统支持率与经济的关系：多维半参数方法AI 解读

Summary Empirical studies analyzing the determinants of US presidential popularity have delivered quite inconclusive results concerning the role of economic variables by assuming linear relationships. We employ penalized spline smoothing in the context of semiparametric additive mixed models and allow for flexible functional forms and thus possible nonlinear effects for the economic determinants. By controlling for the well‐known politically motivated covariables, we find…

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…

完全依从或完全不依从生存研究中的因果推断：半参数变换模型AI 解读

Summary We consider causal inference in randomized survival studies with right-censored outcomes and all-or-nothing compliance, using semiparametric transformation models to estimate the distribution of survival times in treatment and control groups, conditionally on covariates and latent compliance type. Estimands depending on these distributions, e.g. the complier average causal effect, the complier effect on survival beyond time t and the complier quantile effect, are…

非经典测量误差下条件矩模型的半参数估计AI 解读

This paper develops a framework for the analysis of semiparametric conditional moment models with endogenous and mismeasured causes, which is of empirical importance. We show that one set of valid instruments is sufficient to control for both endogeneity and measurement errors of the causes of interest, which has been observed in linear parametric models. Two-step consistent estimators of the parameters of interest are proposed. We also show that the proposed estimators are…