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股票市场数据的真实与伪长记忆性质AI 解读

We test for the presence of long memory in daily stock returns and their squares using a robust semiparametric procedure of Lobato and Robinson. Spurious results can be produced by nonstationarity and aggregation. We address these problems by analyzing subperiods of returns and using individual stocks. The test results show no evidence of long memory in the returns. By contrast, there is strong evidence in the squared returns.

Neyman–Scott 模型与线样带调查中的聚集及丰度估计AI 解读

This paper considers the estimation of clustering parameters and mean species intensity based on likelihood theory for the simplified Neyman-Scott Poisson model, with observations taken from line transect surveys with a Gaussian detection function. The estimators and accompanying standard error expressions are tractable and easy to calculate, and, coming from likelihood methods, often will have high efficiency. Such properties compare favourably with those of existing…

Current status data arise when the only knowledge about the failure time of interest is whether the failure occurs before or after a random monitoring time. We propose to analyse such data by the semiparametric additive hazards model, which specifies that the hazard function for the failure time associated with a set of possibly time-dependent covariates is the sum of an arbitrary baseline hazard function and a regression function of covariates. Under certain conditions on…

纵向数据的半参数随机混合模型AI 解读

We consider inference for a semiparametric stochastic mixed model for longitudinal data. This model uses parametric fixed effects to represent the covariate effects and an arbitrary smooth function to model the time effect and accounts for the within-subject correlation using random effects and a stationary or nonstationary stochastic process. We derive maximum penalized likelihood estimators of the regression coefficients and the nonparametric function. The resulting…

含时变协变量的现状数据局部有效估计AI 解读

In biostatistical applications, interest often focuses on the estimation of the distribution of a failure time variable T. If one observes only whether or not T exceeds an observed monitoring time C, then the data structure is called current status data, also known as interval-censored data, case I. We extend the data structure by allowing the presence of a possibly time-dependent covariate process which is observed up till the monitoring time C. We follow the approach of…

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季节单位根的 Phillips–Perron 型检验AI 解读

In this paper we consider a semiparametric version of the test for seasonal unit roots suggested by Hylleberg, Engle, Granger, and Yoo (1990, Journal of Econometrics 44, 215–238). The asymptotic theory is based on the analysis of a simple regression problem, and the results apply to tests at any given frequency in the range (0,π]. Monte Carlo simulations suggest that the test may have more power than the parametric test of Hylleberg et al. (1990). On the other hand, the…

离散数据的半参数平滑AI 解读

A method for semiparametric smoothing of discrete data is proposed. The method consists of the repeated application of a Markov chain transition matrix constructed so as to have a given standard discrete parametric vehicle model as its stationary distribution. Theory and practical examples suggest that the approach yields improved performance over fully nonparametric methods when the vehicle model is a good one and otherwise provides a method comparable to fully…

竞争风险模型中多种原因别风险率相等的一类检验AI 解读

A class of asymptotically distribution-free tests for the equality of the k risks in a competing risks model is considered. The tests can be derived by martingale theory or by a marginal likelihood approach. When the risks are dependent, the quantities to be compared would be the cause-specific hazard rates or equivalently the pseudosurvivor functions related to the cause-specific hazard rates. This class of tests allows one to compare k risks simultaneously for k > 2, is…

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弱依赖数据下的筛极值估计AI 解读

Many non/semiparametric time series estimates may be regarded as different forms of sieve extremum estimates. For stationary absolute regular mixing observations, the authors obtain convergence rates of sieve extremurn estimates and root-n asymptotic normality of 'plug-in' sieve extremum estimates of smooth functionals. As applications to time series models, they give convergence rates for nonparametric ARX(p,q) regression via neural networks, splines, wavelets; root-n…

倾向得分在平均处理效应有效半参数估计中的作用AI 解读

The role of propensity score in the efficient estimation of the average treatment effects is examined. If the treatment is ignorable given some observed characteristics, it is shown that the propensity score is ancillary for estimation of the average treatment effects but not for estimation of average treatment effects on the treated. Efficient semiparametric estimators take the form of relevant sample averages of the data completed by the nonparametric imputation method.…

非嵌套半参数模型检验：恩格尔曲线设定应用AI 解读

This paper proposes a test statistic for discriminating between two partly non-linear regression models whose parametric components are non-nested. The statistic has the form of a J-test based on a parameter which artificially nests the null and alternative hypotheses. We study in detail the realistic case where all regressors in the non-linear part are discrete and then no smoothing is required on estimating the non-parametric components. We also consider the general case…

非嵌套半参数模型检验：恩格尔曲线设定的应用AI 解读

This paper proposes a test statistic for discriminating between two partly non-linear regression models whose parametric components are non-nested. The statistic has the form of a J-test based on a parameter which artificially nests the null and alternative hypotheses. We study in detail the realistic case where all regressors in the non-linear part are discrete and then no smoothing is required on estimating the non-parametric components. We also consider the general case…

具有灵活协变量效应的半参数生存模型AI 解读

A semiparametric hazard model with parametrized time but general covariate dependency is formulated and analyzed inside the framework of counting process theory. A profile likelihood principle is introduced for estimation of the parameters: the resulting estimator is $n^{1/2}$-consistent, asymptotically normal and achieves the semiparametric efficiency bound. An estimation procedure for the nonparametric part is also given and its asymptotic properties are derived. We…

已知总样本量时偏倚与截断数据的半参数似然推断AI 解读

Summary Biased and truncated data arise in many practical areas. Many efficient statistical methods have been studied in the literature. This paper discusses likelihood-based inferences for the two types of data in the presence of auxiliary information of known total sample size. It is shown that this information improves inference about the underlying distribution and its parameters in which we are interested. A semiparametric likelihood ratio confidence interval technique…

一种半参数多重插补方法AI 解读

Summary In this paper, we describe how to use multiple imputation semiparametrically to obtain estimates of parameters and their standard errors when some individuals have missing data. The methods given require the investigator to know or be able to estimate the process generating the missing data but requires no full distributional form for the data. The method is especially useful for non-standard problems, such as estimating the median when data are missing.

含内生或误测解释变量的半参数潜变量模型估计AI 解读

A simple root n consistent, asymptotically normal semiparametric estimator of the coefficient vector beta in the latent variable specification y = L (beta'x + e) is constructed. The distribution of e is unknown and may be correlated with x or be conditionally heteroscedastic, e.g., x can contain measurement error. The function L can also be unknown. The identification assumption is that e is uncorrelated with instruments u and that the conditional distribution of e given x…

工作经验对女性工资与劳动供给的影响AI 解读

This paper develops and implements a semiparametric estimator for investigating, with panel data, the importance of human capital and time nonseparable preferences to females when aggregate shocks are present. It provides a set of conditions for making statistical inferences about agents' expectations of their correlated future choices, from a short panel. Under the assumption that observed allocations are Pareto optimal, a dynamic model of female labour supply and…

论筛法与惩罚法AI 解读

We develop a general theory which provides a unified treatment for the asymptotic normality and efficiency of the maximum likelihood estimates (MLE's) in parametric, semiparametric and nonparametric models. We find that the asymptotic behavior of substitution estimates for estimating smooth functionals are essentially governed by two indices: the degree of smoothness of the functional and the local size of the underlying parameter space. We show that when the local size of…

随机删失观测下风险函数估计的半参数方法AI 解读

One method for estimating the hazard function is to use a parametric estimator, provided that the underlying distribution of the data can be assumed to belong to some well known family of distributions depending on an unknown, possibly vector-valued parameter. Another approach is to use nonparametric estimators, such as Cox's proportional hazard models, kernel hazard estimators, and so on. However, nonparametric estimators are less efficient than suitably chosen parametric…

半参数效率及其对群组序贯研究设计与分析的意义AI 解读

Authors have shown that the time-sequential joint distributions of many statistics used to analyze data arising from group-sequential time-to-event and longitudinal studies are multivariate normal with an independent increments covariance structure. In Theorem 1 of this article, we demonstrate that this limiting distribution arises naturally when one uses an efficient test statistic to test a single parameter in a semiparametric or parametric model. Because we are able to…

随机删失观测下风险函数估计的半参数方法AI 解读

One method for estimating the hazard function is to use a parametric estimator, provided that the underlying distribution of the data can be assumed to belong to some well known family of distributions depending on an unknown, possibly vector-valued parameter. Another approach is to use nonparametric estimators, such as Cox's proportional hazard models, kernel hazard estimators, and so on. However, nonparametric estimators are less efficient than suitably chosen parametric…

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