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空间样条回归模型AI 解读

Summary We describe a model for the analysis of data distributed over irregularly shaped spatial domains with complex boundaries, strong concavities and interior holes. Adopting an approach that is typical of functional data analysis, we propose a spatial spline regression model that is computationally efficient, allows for spatially distributed covariate information and can impose various conditions over the boundaries of the domain. Accurate surface estimation is achieved…

含非平稳性的三角方程系统半参数估计AI 解读

A system of multivariate semiparametric nonlinear time series models is studied with possible dependence structures and nonstationarities in the parametric and nonparametric components. The parametric regressors may be endogenous while the nonparametric regressors are assumed to be strictly exogenous. The parametric regressors may be stationary or nonstationary and the nonparametric regressors are nonstationary integrated time series. Semiparametric least squares (SLS)…

第一价格拍卖应采用何种进入模型？一种非参数方法AI 解读

We develop a selective entry model for first-price auctions that nests two polar models often estimated in the empirical literature on auctions, Levin and Smith (1994), and Samuelson (1985). The selective entry model features a pro-competitive selection effect. The selection effect is shown to be nonparametrically identifiable, and a nonparametric test for its presence is proposed. This test can be used to discriminate between the two polar models.

贝叶斯半参数多元GARCH建模AI 解读

This paper proposes a Bayesian nonparametric modeling approach for the return distribution in multivariate GARCH models. In contrast to the parametric literature the return distribution can display general forms of asymmetry and thick tails. An infinite mixture of multivariate normals is given a flexible Dirichlet process prior. The GARCH functional form enters into each of the components of this mixture. We discuss conjugate methods that allow for scale mixtures and…

随机临床试验中依方案生存终点疗效的敏感性分析AI 解读

Assessing per-protocol treatment effcacy on a time-to-event endpoint is a common objective of randomized clinical trials. The typical analysis uses the same method employed for the intention-to-treat analysis (e.g., standard survival analysis) applied to the subgroup meeting protocol adherence criteria. However, due to potential post-randomization selection bias, this analysis may mislead about treatment efficacy. Moreover, while there is extensive literature on methods for…

非参数有限混合回归模型AI 解读

Motivated by an analysis of US house price index data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm…

通过幂变换估计极端条件分位数AI 解读

The estimation of extreme conditional quantiles is an important issue in numerous disciplines. Quantile regression (QR) provides a natural way to capture the covariate effects at different tails of the response distribution. However, without any distributional assumptions, estimation from conventional QR is often unstable at the tails, especially for heavy-tailed distributions due to data sparsity. In this article, we develop a new three-stage estimation procedure that…

潜在监督学习AI 解读

training labels which serve as surrogates for the unobserved class labels. A specific model is investigated where the surrogate variable arises from a two-component Gaussian mixture with unknown means and variances, and the component membership is determined by a hyperplane in the covariate space. The estimation of the separating hyperplane and the Gaussian mixture parameters forms what shall be referred to as the change-line classification problem. A data-driven sieve…

任意样本选择偏差下未知对称总体的半参数有效稳健估计AI 解读

We propose semiparametric methods to estimate the center and shape of a symmetric population when a representative sample of the population is unavailable due to selection bias. We allow an arbitrary sample selection mechanism determined by the data collection procedure, and we do not impose any parametric form on the population distribution. Under this general framework, we construct a family of consistent estimators of the center that is robust to population model…

纵向研究中时变变换模型的条件分布与秩追踪概率估计AI 解读

An objective of longitudinal analysis is to estimate the conditional distributions of an outcome variable through a regression model. The approaches based on modeling the conditional means are not appropriate for this task when the conditional distributions are skewed or cannot be approximated by a normal distribution through a known transformation. We study a class of time-varying transformation models and a two-step smoothing method for the estimation of the conditional…

存在非随机缺失的牙周数据非参数空间模型AI 解读

Periodontal disease progression is often quantified by clinical attachment level (CAL) defined as the distance down a tooth's root that is detached from the surrounding bone. Measured at 6 locations per tooth throughout the mouth (excluding the molars), it gives rise to a dependent data set-up. These data are often reduced to a one-number summary, such as the whole mouth average or the number of observations greater than a threshold, to be used as the response in a…

用于局部聚类的非参数贝叶斯模型及其蛋白质组学应用AI 解读

We propose a nonparametric Bayesian local clustering (NoB-LoC) approach for heterogeneous data. NoB-LoC implements inference for nested clusters as posterior inference under a Bayesian model. Using protein expression data as an example, the NoB-LoC model defines a protein (column) cluster as a set of proteins that give rise to the same partition of the samples (rows). In other words, the sample partitions are nested within protein clusters. The common clustering of the…

具有灵活相关效应的面板 Probit：潜在异质性下的技术溢出量化AI 解读

SUMMARY In this paper, we introduce a Bayesian panel probit model with two flexible latent effects: first, unobserved individual heterogeneity that is allowed to vary in the population according to a nonparametric distribution; and second, a latent serially correlated common error component. In doing so, we extend the approach developed in Albert and Chib ( Journal of the American Statistical Association 1993; 88 : 669–679; in Bayesian Biostatistics , Berry DA, Stangl DK…

第一价格采购拍卖中进入成本的非参数估计AI 解读

SUMMARY In this paper, I investigate Samuelson's low‐price auction model with entry costs. The model's equilibrium implies that the distribution of bids is truncated at the threshold for participation. I use the model to estimate the cost of participation in Michigan highway procurement auctions. The null hypothesis of zero entry costs is rejected. Using my empirical results, I then construct an estimate of the optimal auction, which employs regular policy tools such as…

高斯白噪声模型中的非参数 Bernstein–von Mises 定理AI 解读

Bernstein–von Mises theorems for nonparametric Bayes priors in the Gaussian white noise model are proved. It is demonstrated how such results justify Bayes methods as efficient frequentist inference procedures in a variety of concrete nonparametric problems. Particularly Bayesian credible sets are constructed that have asymptotically exact $1-\alpha$ frequentist coverage level and whose $L^{2}$-diameter shrinks at the minimax rate of convergence (within logarithmic factors)…

波动率占用时间AI 解读

We propose nonparametric estimators of the occupation measure and the occupation density of the diffusion coefficient (stochastic volatility) of a discretely observed Itô semimartingale on a fixed interval when the mesh of the observation grid shrinks to zero asymptotically. In a first step we estimate the volatility locally over blocks of shrinking length, and then in a second step we use these estimates to construct a sample analogue of the volatility occupation time and a…

构造函数非参数置信带的简单 Bootstrap 方法AI 解读

Standard approaches to constructing nonparametric confidence bands for functions are frustrated by the impact of bias, which generally is not estimated consistently when using the bootstrap and conventionally smoothed function estimators. To overcome this problem it is common practice to either undersmooth, so as to reduce the impact of bias, or oversmooth, and thereby introduce an explicit or implicit bias estimator. However, these approaches, and others based on…

This paper investigates identification and root-n-consistent estimation of a class of single-index panel data models in which the link function is unknown, the unobserved individual effects may be correlated with all the explanatory variables, and all the explanatory variables may be predetermined. We propose two sets of sufficient conditions, one in which link function is assumed to be strictly increasing, and the other in which it is not. We propose simple kernel-based…

含未观测协变量的非线性动态面板模型：识别与估计AI 解读

This paper considers nonparametric identification of nonlinear dynamic models for panel data with unobserved covariates. Including such unobserved covariates may control for both the individual-specific unobserved heterogeneity and the endogeneity of the explanatory variables. Without specifying the distribution of the initial condition with the unobserved variables, we show that the models are nonparametrically identified from two periods of the dependent variable Yit and…

时间序列条件分位数的非参数推断AI 解读

This paper considers model-free hypothesis testing and confidence interval construction for conditional quantiles of time series. A new method, which is based on inversion of the smoothed empirical likelihood of the conditional distribution function around the local polynomial estimator, is proposed. The associated inferential procedures do not require variance estimation, and the confidence intervals are automatically shaped by data. We also construct the bias-corrected…

无限方差时间序列的预期损失估计与推断AI 解读

We study estimation and inference of the expected shortfall for time series with infinite variance. Both the smoothed and nonsmoothed estimators are investigated. The rate of convergence is determined by the tail thickness parameter, and the limiting distribution is in the stable class with parameters depending on the tail thickness parameter of the time series and on the dependence structure, which makes inference complicated. A subsampling procedure is proposed to carry…

税收对增长的非线性影响：使用平均边际税率的半参数方法AI 解读

SUMMARY One of the major challenges of empirical tax research is the identification and calculation of appropriate tax data. While there is consensus that average marginal tax rates are most suitable for studying the effects of tax policy on economic growth, because of data limitations the calculation of marginal tax rates has been limited to the USA and the UK. This paper provides calculations of average marginal tax rates for the four Scandinavian countries using the…

带半参数个体效应的非线性面板模型估计AI 解读

This paper investigates identification and estimation of a class of nonlinear panel data, single-index models. The model allows for unknown time-specific link functions, and semiparametric specification of the individual-specific effects. We develop an estimator for the parameters of interest, and propose a powerful new kernel-based modified backfitting algorithm to compute the estimator. We derive uniform rates of convergence results for the estimators of the link…

基于倾向得分的估计量表现AI 解读

We investigate the finite sample properties of a large number of estimators for the average treatment effect on the treated that are suitable when adjustment for observed covariates is required, like inverse probability weighting, kernel and other variants of matching, as well as different parametric models. The simulation design used is based on real data usually employed for the evaluation of labour market programmes in Germany. We vary several dimensions of the design…