论文检索

竞争性终止事件下重复标记过程的效用测量评估AI 解读

Yifei Suna* & Mei-Cheng Wangaa Department of Biostatistics, Johns Hopkins University, Baltimore, MDCONTACT Yifei Sun ysun26@jhu.edu Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/r/JASA.Supplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.ABSTRACTIn follow-up…

双变量区间删失失效时间回归的筛半参数极大似然方法AI 解读

Interval-censored failure time data arise in a number of fields and many authors have discussed various issues related to their analysis. However, most of the existing methods are for univariate data and there exists only limited research on bivariate data, especially on regression analysis of bivariate interval-censored data. We present a class of semiparametric transformation models for the problem and for inference, a sieve maximum likelihood approach is developed. The…

基于距离协方差的独立成分分析AI 解读

This article introduces a novel statistical framework for independent component analysis (ICA) of multivariate data. We propose methodology for estimating mutually independent components, and a versatile resampling-based procedure for inference, including misspecification testing. Independent components are estimated by combining a nonparametric probability integral transformation with a generalized nonparametric whitening method based on distance covariance that…

A topological multiple testing scheme is presented for detecting peaks in images under stationary ergodic Gaussian noise, where tests are performed at local maxima of the smoothed observed signals. The procedure generalizes the one-dimensional scheme of [31] to Euclidean domains of arbitrary dimension. Two methods are developed according to two different ways of computing p-values: (i) using the exact distribution of the height of local maxima, available explicitly when the…

时间序列核平滑预渐近推断的固定带宽视角AI 解读

This paper develops robust testing procedures for nonparametric kernel methods in the presence of temporal dependence of unknown forms. Based on the fixed-bandwidth asymptotic variance and the pre-asymptotic variance, we propose a heteroskedasticity and autocorrelation robust (HAR) variance estimator that achieves double robustness — it is asymptotically valid regardless of whether the temporal dependence is present or not, and whether the kernel smoothing bandwidth is held…

基于三角密度混合的贝叶斯众数回归AI 解读

Bayesian semiparametric models for mean and median regressions abound, but a void for mode regressions exists. We fill this gap by nonparametrically modeling the error distribution in such regressions that entails constructing prior distributions on densities which exhibit flexibility, while fixing the mode at 0. Such priors exist when constraining the mean and median but, to our knowledge, there is none for the mode. Our solution with mixtures of triangular distributions…

非可分面板数据模型识别假设的检验AI 解读

Recent work on nonparametric identification of average partial effects (APEs) from panel data require restrictions on individual or time heterogeneity. Identifying assumptions under the “generalized first-differencing” category, such as time homogeneity (Chernozhukov et al., 2013), have testable equality restrictions on the distribution of the outcome variable. This paper proposes specification tests based on these restrictions. The bootstrap critical values for the…

积分波动率及相关过程的有效估计AI 解读

We derive nonparametric efficiency bounds for regular estimators of integrated smooth transformations of instantaneous variances, in particular, integrated power variance. We find that realized variance attains the efficiency bound for integrated variance under both regular and irregular sampling schemes. For estimating higher powers such as integrated quarticity, the block-based procedures of Mykland and Zhang (2009) can get arbitrarily close to the nonparametric bounds,…

利用高频测度推断价格与波动率的自激跳跃AI 解读

Summary Dynamic jumps in the price and volatility of an asset are modelled using a joint Hawkes process in conjunction with a bivariate jump diffusion. A state‐space representation is used to link observed returns, plus nonparametric measures of integrated volatility and price jumps, to the specified model components, with Bayesian inference conducted using a Markov chain Monte Carlo algorithm. An evaluation of marginal likelihoods for the proposed model relative to a large…

俱乐部趋同的千禧年高峰：国家财富分布变化的新观察AI 解读

Summary This paper proposes an easy‐to‐use nonparametric indicator for club convergence, or convergence within clusters of countries: it measures whether the modes of the gross domestic product (GDP) per capita distribution become more pronounced over time. Relying on changes in the critical bandwidth for unimodality, the indicator is a dynamic extension of concepts from often‐used multimodality tests. Its evolution suggests the new empirical result of a ‘millennium peak’ in…

经验贝叶斯棒球再论：纵向数据的经验贝叶斯方法AI 解读

Summary Empirical Bayes methods for Gaussian and binomial compound decision problems involving longitudinal data are considered. A recent convex optimization reformulation of the nonparametric maximum likelihood estimator of Kiefer and Wolfowitz ( Annals of Mathematical Statistics 1956; 27 : 887–906) is employed to construct nonparametric Bayes rules for compound decisions. The methods are illustrated with an application to predict baseball batting averages, and the age…

含边界问题的半参数模型伪似然推断AI 解读

Consider a semiparametric model indexed by a Euclidean parameter of interest and an infinite-dimensional nuisance parameter. In many applications, pseudolikelihood provides a convenient way to infer the parameter of interest, where the nuisance parameter is replaced by a consistent estimator. The purpose of this paper is to establish the asymptotic behaviour of the pseudolikelihood ratio statistic under semiparametric models. In particular, we consider testing the hypothesis…

带趋势ARMA时间序列的预言效率估计与一致模型选择AI 解读

Summary Most time series that are encountered in practice contain non-zero trend, yet textbook approaches to time series analysis are typically focused on zero-mean stationary auto-regressive moving average (ARMA) processes. Trend is often estimated by ad hoc methods and subtracted from time series, and the residuals are used as the true ARMA noise for data analysis and inference, including parameter estimation, lag selection and prediction. We propose a theoretically…

计算机模型校准的频率学派方法AI 解读

Summary The paper considers the computer model calibration problem and provides a general frequentist solution. Under the framework proposed, the data model is semiparametric with a non-parametric discrepancy function which accounts for any discrepancy between physical reality and the computer model. In an attempt to solve a fundamentally important (but often ignored) identifiability issue between the computer model parameters and the discrepancy function, the paper proposes…

利用形状约束估计思想检验线性回归模型AI 解读

Summary A formal likelihood ratio hypothesis test for the validity of a parametric regression function is proposed, using a large dimensional, non-parametric double-cone alternative. For example, the test against a constant function uses the alternative of increasing or decreasing regression functions, and the test against a linear function uses the convex or concave alternative. The test proposed is exact and unbiased and the critical value is easily computed. The power of…

竞争风险累积发生率半参数变换模型的有效估计AI 解读

Summary The cumulative incidence is the probability of failure from the cause of interest over a certain time period in the presence of other risks. A semiparametric regression model proposed by Fine and Gray has become the method of choice for formulating the effects of covariates on the cumulative incidence. Its estimation, however, requires modelling of the censoring distribution and is not statistically efficient. We present a broad class of semiparametric transformation…

混合数据的高维半参数潜在图模型AI 解读

Summary We propose a semiparametric latent Gaussian copula model for modelling mixed multivariate data, which contain a combination of both continuous and binary variables. The model assumes that the observed binary variables are obtained by dichotomizing latent variables that satisfy the Gaussian copula distribution. The goal is to infer the conditional independence relationship between the latent random variables, based on the observed mixed data. Our work has two main…

基于深度的极端分位数区域估计AI 解读

Summary Consider the extreme quantile region induced by the half-space depth function HD of the form Q={x∈Rd:HD(x,P)⩽β}, such that PQ=p for a given, very small p > 0. Since this involves extrapolation outside the data cloud, this region can hardly be estimated through a fully non-parametric procedure. Using extreme value theory we construct a natural semiparametric estimator of this quantile region and prove a refined consistency result. A simulation study clearly…

内生采样时点下积分二次协变动的估计AI 解读

When estimating high-frequency covariance (quadratic covariation) of two arbitrary assets observed asynchronously, simple assumptions, such as independence, are usually imposed on the relationship between the prices process and the observation times. In this paper, we introduce a general endogenous two-dimensional nonparametric model. Because an observation is generated whenever an auxiliary process called observation time process hits one of the two boundary processes, it…

似然比有序约束下的两样本密度估计AI 解读

In this paper, we propose a method for estimating the probability density functions in a two-sample problem where the ratio of the densities is monotone. This problem has been widely identified in the literature, but effective solution methods, in which the estimates should be probability densities and the corresponding density ratio should inherit monotonicity, are unavailable. If these conditions are not satisfied, the applications of the resultant density estimates might…

区间删失失效时间数据的病例队列研究AI 解读

The case-cohort design has been widely used as a means of cost reduction in assembling or measuring expensive covariates in large cohort studies. The existing literature on the case-cohort design is mainly focused on right-censored data. In practice, however, the failure time is often subject to interval-censoring; it is known only to fall within some random time interval. In this paper, we consider the case-cohort study design for interval-censored failure time and develop…

具有半参数相关效应的惩罚分位数回归：异质性偏好应用AI 解读

Summary This paper proposes new ℓ 1 ‐penalized quantile regression estimators for panel data, which explicitly allows for individual heterogeneity associated with covariates. Existing fixed‐effects estimators can potentially suffer from three limitations which are overcome by the proposed approach: (i) incidental parameters bias in nonlinear models with large N and small T ; (ii) lack of efficiency; and (iii) inability to estimate the effects of time‐invariant regressors. We…

小单位中的群体隔离测度：部分识别分析AI 解读

We consider the issue of measuring segregation in a population of small units, considering establishments in our application. Each establishment may have a different probability of hiring an individual from the minority group. We define segregation indices as inequality indices on these unobserved, random probabilities. Because these probabilities are measured with error by proportions, standard estimators are inconsistent. We model this problem as a nonparametric binomial…

竞买人不对称的升价拍卖AI 解读

We present a partial identification approach for ascending auctions with bidder asymmetries, where bidders' asymmetric types may be unobservable to the econometrician. Our approach yields sharp bounds and builds on and generalizes other recent bounds approaches for correlated private values ascending auctions. When bidder identities are observable, our approach yields tighter bounds than previous approaches that ignore asymmetry, demonstrating that bidder asymmetries can…