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非参数非平稳性检验AI 解读

We propose additive functional-based nonstationarity tests that exploit the different divergence rates of the occupation times of a (possibly nonlinear) process under the null of nonstationarity (stationarity) versus the alternative of stationarity (nonstationarity). We consider both discrete-time series and continuous-time processes. The discrete-time case covers Harris recurrent Markov chains and integrated processes. The continuous-time case focuses on Harris recurrent…

线性过程加权和的渐近正态性AI 解读

We establish asymptotic normality of weighted sums of linear processes with general triangular array weights and when the innovations in the linear process are martingale differences. The results are obtained under minimal conditions on the weights and innovations. We also obtain weak convergence of weighted partial sum processes. The results are applicable to linear processes that have short or long memory or exhibit seasonal long memory behavior. In particular, they are…

密度加权平均导数的小带宽渐近理论AI 解读

This paper proposes (apparently) novel standard error formulas for the density-weighted average derivative estimator of Powell, Stock, and Stoker ( Econometrica 57, 1989). Asymptotic validity of the standard errors developed in this paper does not require the use of higher-order kernels, and the standard errors are “robust” in the sense that they accommodate (but do not require) bandwidths that are smaller than those for which conventional standard errors are valid.…

固定效应面板变系数模型的直接半参数估计AI 解读

In this paper, we present a new technique to estimate varying coefficient models of unknown form in a panel data framework where individual effects are arbitrarily correlated with the explanatory variables in an unknown way. The estimator is based on first differences and then a local linear regression is applied to estimate the unknown coefficients. To avoid a non‐negligible asymptotic bias, we need to introduce a higher‐dimensional kernel weight. This enables us to remove…

固定效应面板部分线性可加回归模型的估计AI 解读

In this paper, we investigate the estimation problem of fixed effects panel data partially linear additive regression models. Semi‐parametric fixed effects panel data regression models are tools that are well suited to econometric analysis and the analysis of cDNA micro‐arrays. By applying a polynomial spline series approximation and a profile least‐squares procedure, we propose a semi‐parametric least‐squares dummy variables estimator (SLSDVE) for the parametric component…

半参数线性模型中的贝叶斯变量选择AI 解读

There is a rich literature on Bayesian variable selection for parametric models. Our focus is on generalizing methods and asymptotic theory established for mixtures of g-priors to semiparametric linear regression models having unknown residual densities. Using a Dirichlet process location mixture for the residual density, we propose a semiparametric g-prior which incorporates an unknown matrix of cluster allocation indicators. For this class of priors, posterior computation…

利用高频数据估计杠杆效应AI 解读

The leverage effect has become an extensively studied phenomenon that describes the (usually) negative relation between stock returns and their volatility. Although this characteristic of stock returns is well acknowledged, most studies of the phenomenon are based on cross-sectional calibration with parametric models. On the statistical side, most previous works are conducted over daily or longer return horizons, and few of them have carefully studied its estimation,…

利用横截面队列生存数据估计加拿大老年人痴呆终生风险AI 解读

Dementia is one of the world's major public health challenges. The lifetime risk of dementia is the proportion of individuals who ever develop dementia during their lifetime. Despite its importance to epidemiologists and policy-makers, this measure does not seem to have been estimated in the Canadian population. Data from a birth cohort study of dementia are not available. Instead, we must rely on data from the Canadian Study of Heath and Aging, a large cross-sectional study…

多变量数据多重变点分析的非参数方法AI 解读

Change point analysis has applications in a wide variety of fields. The general problem concerns the inference of a change in distribution for a set of time-ordered observations. Sequential detection is an online version in which new data are continually arriving and are analyzed adaptively. We are concerned with the related, but distinct, offline version, in which retrospective analysis of an entire sequence is performed. For a set of multivariate observations of arbitrary…

识别个体多发性硬化患者 MRI 病灶计数的异常增加AI 解读

Data Safety and Monitoring Boards (DSMBs) for multiple sclerosis clinical trials consider an increase of contrast-enhancing lesions on repeated magnetic resonance imaging an indicator for potential adverse events. However, there are no published studies that clearly identify what should be considered an “unexpected increase” of lesion activity for a patient. To address this problem, we consider as an index the likelihood of observing lesion counts as large as those observed…

高维元椭圆数据的尺度不变稀疏主成分分析AI 解读

We propose a semiparametric method for conducting scale-invariant sparse principal component analysis (PCA) on high dimensional non-Gaussian data. Compared with sparse PCA, our method has weaker modeling assumption and is more robust to possible data contamination. Theoretically, the proposed method achieves a parametric rate of convergence in estimating the parameter of interests under a flexible semiparametric distribution family; Computationally, the proposed method…

利用正交条件估计常微分方程参数AI 解读

Differential equations are commonly used to model dynamical deterministic systems in applications. When statistical parameter estimation is required to calibrate theoretical models to data, classical statistical estimators are often confronted to complex and potentially ill-posed optimization problem. As a consequence, alternative estimators to classical parametric estimators are needed for obtaining reliable estimates. We propose a gradient matching approach for the…

两阶段队列研究中半参数变换模型的有效估计AI 解读

Under two-phase cohort designs, such as case-cohort and nested case-control sampling, information on observed event times, event indicators, and inexpensive covariates is collected in the first phase, and the first-phase information is used to select subjects for measurements of expensive covariates in the second phase; inexpensive covariates are also used in the data analysis to control for confounding and to evaluate interactions. This paper provides efficient estimation…

随机临床试验中生存率与治疗效应的界标估计AI 解读

In many studies with a survival outcome, it is often not feasible to fully observe the primary event of interest. This often leads to heavy censoring and thus, difficulty in efficiently estimating survival or comparing survival rates between two groups. In certain diseases, baseline covariates and the event time of nonfatal intermediate events may be associated with overall survival. In these settings, incorporating such additional information may lead to gains in efficiency…

单位区间核密度估计的Probit变换方法AI 解读

Kernel estimation of a probability density function supported on the unit interval has proved difficult, because of the well-known boundary bias issues a conventional kernel density estimator would necessarily face in this situation. Transforming the variable of interest into a variable whose density has unconstrained support, estimating that density, and obtaining an estimate of the density of the original variable through back-transformation, seems a natural idea to easily…

基于梯度的核回归充分降维AI 解读

This article proposes a novel approach to linear dimension reduction for regression using nonparametric estimation with positive-definite kernels or reproducing kernel Hilbert spaces (RKHSs). The purpose of the dimension reduction is to find such directions in the explanatory variables that explain the response sufficiently: this is called sufficient dimension reduction. The proposed method is based on an estimator for the gradient of the regression function considered for…

分位数回归模型的贝叶斯信息准则选择AI 解读

Bayesian information criterion (BIC) is known to identify the true model consistently as long as the predictor dimension is finite. Recently, its moderate modifications have been shown to be consistent in model selection even when the number of variables diverges. Those works have been done mostly in mean regression, but rarely in quantile regression. The best-known results about BIC for quantile regression are for linear models with a fixed number of variables. In this…

二次距离拟合优度检验的核、自由度与功效性质AI 解读

In this article, we study the power properties of quadratic-distance-based goodness-of-fit tests.First, we introduce the concept of a root kernel and discuss the considerations that enter the selection of this kernel.We derive an easy to use normal approximation to the power of quadratic distance goodness-of-fit tests and base the construction of a noncentrality index, an analogue of the traditional noncentrality parameter, on it.This leads to a method akin to the…

广义线性模型中均值与误差分布的联合估计AI 解读

This article introduces a semiparametric extension of generalized linear models that is based on a full probability model, but does not require specification of an error distribution or variance function for the data. The approach involves treating the error distribution as an infinite-dimensional parameter, which is then estimated simultaneously with the mean-model parameters using a maximum empirical likelihood approach. The resulting estimators are shown to be consistent…

含非平稳 GARCH 方程的条件相关模型AI 解读

In this article, we investigate the effects of careful modeling the long-run dynamics of the volatilities of stock market returns on the conditional correlation structure. To this end, we allow the individual unconditional variances in conditional correlation generalized autoregressive conditional heteroscedasticity (CC-GARCH) models to change smoothly over time by incorporating a nonstationary component in the variance equations such as the spline-GARCH model and the…

洛伦兹占优的一致非参数检验AI 解读

This article proposes consistent nonparametric methods for testing the null hypothesis of Lorenz dominance. The methods are based on a class of statistical functionals defined over the difference between the Lorenz curves for two samples of welfare-related variables. We present two specific test statistics belonging to the general class and derive their asymptotic properties. As the limiting distributions of the test statistics are nonstandard, we propose and justify…

利用非参数方法高效估计有限总体非线性参数AI 解读

Summary Currently, high precision estimation of non-linear parameters such as Gini indices, low income proportions or other measures of inequality is particularly crucial. We propose a general class of estimators for such parameters that take into account univariate auxiliary information assumed to be known for every unit in the population. Through a non-parametric model-assisted approach, we construct a unique system of survey weights that can be used to estimate any…

连续结局两阶段概率依赖抽样的半参数推断AI 解读

Summary Multiphased designs and biased sampling designs are two of the well-recognized approaches to enhance study efficiency. We propose a new and cost-effective sampling design, the two-phase probability-dependent sampling design, for studies with a continuous outcome. This design will enable investigators to make efficient use of resources by targeting more informative subjects for sampling. We develop a new semiparametric empirical likelihood inference method to take…

多元混合模型成分数的非参数识别与估计AI 解读

Summary We analyse the identifiability of the number of components in k-variate, M-component finite mixture models in which each component distribution has independent marginals, including models in latent class analysis. Without making parametric assumptions on the component distributions, we investigate how one can identify the number of components from the distribution function of the observed data. When k⩾2, a lower bound on the number of components (M) is…