Nonlinear regression modeling via the lasso-type regularization

We consider the problem of constructing nonlinear regression models with Gaussian basis functions, using lasso regularization. Regularization with a lasso penalty is an advantageous in that it estimates some coefficients in linear regression models to be exactly zero. We propose imposing a weighted lasso penalty on a nonlinear regression model and thereby selecting the number of basis functions effectively. In order to select tuning parameters in the regularization method, we use a deviance information criterion proposed by Spiegelhalter et al. (2002), calculating the effective number of parameters by Gibbs sampling. Simulation results demonstrate that our methodology performs well in various situations.     

Correcting the Errors: Volatility Forecast Evaluation Using High‐Frequency Data and Realized Volatilities

We develop general model‐free adjustment procedures for the calculation of unbiased volatility loss functions based on practically feasible realized volatility benchmarks. The procedures, which exploit recent nonparametric asymptotic distributional results, are both easy‐to‐implement and highly accurate in empirically realistic situations. We also illustrate that properly accounting for the measurement errors in the volatility forecast evaluations reported in the existing literature can result in markedly higher estimates for the true degree of return volatility predictability.     

金融结构对产业结构影响的时变特征研究——基于SUR和TVPSS模型的实证检验

金融结构与产业结构的关系一直是学术界的研究热点.文章利用中国1998—2017年的年度数据,构建似不相关回归模型从金融结构规模、效率及深化的角度分析金融结构对产业结构合理化和高级化的影响,建立时变参数状态空间模型描绘了金融结构对产业结构合理化和高级化的动态冲击.实证分析结果表明:金融结构规模提高产业结构合理化水平,促进了产业结构高级化;金融结构效率提高产业结构合理化水平,抑制了产业结构高级化;金融结构深化降低产业结构合理化水平,促进了产业结构高级化.金融结构规模、金融结构效率及金融结构深化对产业结构合理化和高级化的冲击均呈现出时变特征;金融结构对产业结构合理化的影响滞后于其对产业结构高级化的影响.金融结构对产业结构的冲击波幅呈现出前期波动大、后期较为平缓的状态,部分金融结构变量对产业结构的动态冲击呈现出"长尾"现象.当前的中国金融结构已经不适合当前的产业结构,需调整金融结构,以提升产业结构合理化水平和高级化水平.     

结合 SUＲ 与因子效应原则的多响应质量设计

针对多响应的质量设计问题，本文结合似不相关回归(seemingly unrelated regression， SUＲ) 模型与因子效应原则提出了一种新的建模与优化方法． 该方法不仅结合 SUＲ 模型与因子效应原则筛选出各响应模型的显著性变量，而且运用多变量过程能力指数衡量了过程能力满足规格要求程度的水平． 此外，该方法还通过贝叶斯抽样技术考虑了模型参数不确定性和预测响应值波动对优化结果的影响． 首先，在 SUＲ 模型中针对每个变量设置了一个二元变量指示器以考虑因子效应原则，通过所构建的混合二元变量指示器修正了过程响应和试验因子之间的函数关系; 其次，通过计算混合二元变量指示器和模型结构的后验概率以识别显著性变量，从而确定最佳的模型结构; 然后，在此基础上结合贝叶斯抽样技术构建了一种新的多变量过程能力指数，并通过最大化所构建的多变量过程能力指数获得了最佳的参数设计值; 最后，实际案例研究表明: 本文所提方法不仅能够有效地筛选出多响应过程的显著性变量，而且能够获得最佳的参数设计值．     

关于二阶非线性微分方程全局渐近稳定的充要条件

本文研究了二阶非线性微分方程x″+f(x,x′)=0全局渐近稳定的充要条件,并推广了文[5](8][9]中的定理。     

Local influence of nonlinear mixed effects model based on M-estimation

Abstract

In this work we mainly study the local influence in nonlinear mixed effects model with M-estimation. A robust method to obtain maximum likelihood estimates for parameters is presented, and the local influence of nonlinear mixed models based on robust estimation (M-estimation) by use of the curvature method is systematically discussed. The counting formulas of curvature for case weights perturbation, response variable perturbation and random error covariance perturbation are derived. Simulation studies are carried to access performance of the methods we proposed. We illustrate the diagnostics by an example presented in Davidian and Giltinan, which was analyzed under the non-robust situation.     

On hsu's model in regression analysis

The paper examplifies with Hsu’s model a general pattern as how to derive results of variance component estimation from well known results on mean estimation, as far as linear model theory is concerned. This ’ dispersion-mean-correspondence‘provides new and short proofs for various theorems from the literature, concerning unbiased invariant quadratic estimators with minimum BAYES risk or minimum variance. For pure variance component models, unbiased non-negative quadratic estimability is characterized in terms of the design matrices.     

Estimation of factor-augmented panel regressions with weakly influential factors

The use of factor-augmented panel regressions has become very popular in recent years. Existing methods for such regressions require that the common factors are strong, an assumption that is likely to be mistaken in practice. Motivated by this, the current article offers an analysis of the effect of weak, semi-weak, and semi-strong factors on two of the most popular estimators for factor-augmented regressions, namely, principal components (PC) and common correlated effects (CCE).     

Estimation of a rare sensitive attribute for two-stage randomized response model in probability proportional to size sampling using Poisson probability distribution

This paper addresses the estimation of the mean number of individuals in the population who possess a rare sensitive attribute using Poisson distribution for the situations of (i) clustered population and (ii) stratified population with clusters are strata units. Properties of the proposed estimation procedures have been discussed when the proportion of a rare unrelated non-sensitive attribute is assumed to be known as well as unknown. Empirical studies are carried out to support the theoretical results which showed dominance over Lee et al. [Estimation of a rare sensitive attribute in probability proportional to size measures using Poisson distribution. Statistics (Ber). 2014;48(3):685–709] estimation procedures.     

Business-Cycle Analysis With a Markov-Switching Model

This article explores the Hamilton Markov-switching model through an analysis of the business cycles of eight developed market economies. Forecasting and specification tests suggest only marginal improvements over linear autoregressive models. Yet filtered and smoothed conditional probabilities indicate turning points in business cycles that closely correlate with turning points from traditional methods. Tests regarding the asymmetry of business cycles reject the null of symmetry for most countries.