Local estimation for varying-coefficient models with longitudinal data

Varying-coefficient models are very useful for longitudinal data analysis. In this paper, we focus on varying-coefficient models for longitudinal data. We develop a new estimation procedure using Cholesky decomposition and profile least squares techniques. Asymptotic normality for the proposed estimators of varying-coefficient functions has been established. Monte Carlo simulation studies show excellent finite-sample performance. We illustrate our methods with a real data example.     

Confidence distributions applied to propagating uncertainty to inference based on estimating the local false discovery rate: A fiducial continuum from confidence sets to empirical Bayes set estimates as the number of comparisons increases

Empirical Bayes estimates of the local false discovery rate can reflect uncertainty about the estimated prior by supplementing their Bayesian posterior probabilities with confidence levels as posterior probabilities. This use of coherent fiducial inference with hierarchical models generates set estimators that propagate uncertainty to varying degrees. Some of the set estimates approach estimates from plug-in empirical Bayes methods for high numbers of comparisons and can come close to the usual confidence sets given a sufficiently low number of comparisons.     

Piecewise FARIMA models for long-memory time series

We consider the problem of modelling a long-memory time series using piecewise fractional autoregressive integrated moving average processes. The number as well as the locations of structural break points (BPs) and the parameters of each regime are assumed to be unknown. A four-step procedure is proposed to find out the BPs and to estimate the parameters of each regime. Its effectiveness is shown by Monte Carlo simulations and an application to real traffic data modelling is considered.     

Local logisitic regression an application to army penetration data

There have been a number of procedures used to analyze non-monotonic binary data to predict the probability of response. Some classical procedures are the Up and Down strategy, the Robbins–Monro procedure, and other sequential optimization designs. Recently, nonparametric procedures such as kernel regression and local linear regression (llogr) have been applied to this type of data. It is a well known fact that kernel regression has problems fitting the data near the boundaries and a drawback with local linear regression is that it may be “too linear” when fitting data from a curvilinear function. The procedure introduced in this paper is called local logistic regression, which fits a logistic regression function at each of the data points. An example is given using United States Army projectile data that supports the use of local logistic regression when analyzing non-monotonic binary data for certain response curves. Properties of local logistic regression will be presented along with simulation results that indicate some of the strengths of the procedure.     

Convergence rates for uniform confidence intervals based on local polynomial regression estimators

We investigate the convergence rates of uniform bias-corrected confidence intervals for a smooth curve using local polynomial regression for both the interior and boundary region. We discuss the cases when the degree of the polynomial is odd and even. The uniform confidence intervals are based on the volume-of-tube formula modified for biased estimators. We empirically show that the proposed uniform confidence intervals attain, at least approximately, nominal coverage. Finally, we investigate the performance of the volume-of-tube based confidence intervals for independent non-Gaussian errors.     

A decade after the Reform: political activism of the Chinese of West Kalimantan,Indonesia

Political reform after the departure of President Soeharto’s New Order (1966–1998) provided opportunities for previously oppressed social groups to express their concerns and to demand fair recognition. The results of this newly found freedom have been quite immediately visible in Jakarta, where social and political institutions spearheaded by Chinese originally sprouted. In the regions, political participation of ethnic Chinese has also grown; significantly in those regions with a large Chinese population. In West Kalimantan, the number of Chinese being elected to local parliaments in some regions has doubled. They have also contested numerous direct local executive elections since 2003 and have been successful in winning four posts: a mayor, a district head, a deputy district head, and a deputy governor. By looking at the case of West Kalimantan, this article will examine the factors behind the growth in Chinese political activism, the factors contributing to the success of Chinese candidates in elections, how the Chinese have influenced local and provincial politics, and the challenges they are facing.     

Estimation and diagnostic analysis in skew-generalized-normal regression models

The skew-generalized-normal distribution [Arellano-Valle, RB, Gómez, HW, Quintana, FA. A new class of skew-normal distributions. Comm Statist Theory Methods 2004;33(7):1465–1480] is a class of asymmetric normal distributions, which contains the normal and skew-normal distributions as special cases. The main virtues of this distribution is that it is easy to simulate from and it also supplies a genuine expectation–maximization (EM) algorithm for maximum likelihood estimation. In this paper, we extend the EM algorithm for linear regression models assuming skew-generalized-normal random errors and we develop a diagnostics analyses via local influence and generalized leverage, following Zhu and Lee's approach. This is because Cook's well-known approach would be more complicated to use to obtain measures of local influence. Finally, results obtained for a real data set are reported, illustrating the usefulness of the proposed method.     

Spatially Balanced Sampling of Continuous Populations

When sampling from a continuous population (or distribution), we often want a rather small sample due to some cost attached to processing the sample or to collecting information in the field. Moreover, a probability sample that allows for design‐based statistical inference is often desired. Given these requirements, we want to reduce the sampling variance of the Horvitz–Thompson estimator as much as possible. To achieve this, we introduce different approaches to using the local pivotal method for selecting well‐spread samples from multidimensional continuous populations. The results of a simulation study clearly indicate that we succeed in selecting spatially balanced samples and improve the efficiency of the Horvitz–Thompson estimator.     

我国学前教育地方立法研究

学前教育地方立法是我国学前教育立法体系的有机组成部分，截至2020 年1 月，我国现行有效的学前教育地方性法规 规章共61 部，呈现出立法时间跨度大，立法主体覆盖面广，立法模式体例多样等特征。地方立法一定程度上规范着地方学前 教育的有序开展，但也存在部分省市立法机关对当地学前教育立法关注度不高、部分立法难以满足当前学前教育发展需求、表 达不规范、可操作性欠佳、缺乏地方特色以及冲突与越位等问题。对此，应从科学确定学前教育地方立法内容、建立地方性法 律适时更新制度、健全学前教育地方立法调研制度、加强地方立法专业化制度建设等方面予以优化。     

土地流转中为什么会形成大量小农复制

土地流转是实现规模化经营的前提之一,然而土地流转中却出现大量小农复制,从而影响土地流转的质量和效果。运用12省(自治区)2 553份农户问卷,详细分析土地流转中小农复制的现状及其形成原因。研究发现：(1)农户流转耕地面积不大,转入户流转面积20亩及以下的约占总转入户数的60.3％,转出户流转面积5亩及以下的约占总转出户数的74.38％,转出户中仍然经营土地的约占总转出户数的74％。(2)农业分工发达使生产更加方便、文化传统形成路径依赖、禀赋效应产生“价值幻觉”以及公共服务不健全使未来存在不确定性导致转出户流转部分土地。乡土社会限制土地流转范围和经营风险增加风险感知导致转入户流转小规模土地。