Nonparametric estimation of a two dimensional continuous–discrete density function by wavelets

We consider the estimation of a two dimensional continuous–discrete density function. A new methodology based on wavelets is proposed. We construct a linear wavelet estimator and a non-linear wavelet estimator based on a term-by-term thresholding. Their rates of convergence are established under the mean integrated squared error over Besov balls. In particular, we prove that our adaptive wavelet estimator attains a fast rate of convergence. A simulation study illustrates the usefulness of the proposed estimators.     

中国城镇居民通信消费的影响因素研究

借助中国2001—2012年省级城镇面板数据,运用动态面板的广义矩估计方法（ GMM）,研究城镇居民通信消费的影响因素。研究结果显示,城镇居民通信消费存在较强的惯性作用,城镇居民人均收入、通信行业消费价格指数、实际利率均对居民通信消费有显著的正向影响,劳动年龄人口的人均个人所得税、恩格尔系数、少儿抚养比、老年抚养比均对通信消费有负向的显著影响。研究结论表明,促进经济发展,提高居民收入是推动通信行业进一步发展的主要动力;同时,也不能忽视人口的因素,要提高多元化的服务内容,满足不同经济群体、不同年龄段人群的通信消费。     

政府规模对经济增长影响效应的GMM评价--基于市级面板数据的实证分析

政府规模会对经济增长产生直接和间接影响,其过度膨胀是一个世界性的难题。为考察我国政府规模对经济增长的影响,基于市级面板数据,应用GMM方法对政府规模的经济增长效应进行动态评价,检验政府规模对经济增长的影响。结论表明：政府规模的扩张对经济增长产生显著负效应,为促进经济发展,必须缩减政府规模。     

我国林木生物质能源资源分类及总量估算

林木生物质能源具有很大的开发价值和利用潜力。目前我国林木生物质能源的发展尚处于起步阶段,资源分类及资源量的估计方法也有待统一和完善。基于对林木生物质能源资源的概念界定及其主要来源,对林木生物质能源资源进行分类,并采用自下而上的估计方法,结合我国最新的森林资源清查数据,分析林木生物质能源资源各组成部分的资源量和可利用情况。     

区域新知识创造对创业活动影响的空间动态研究

基于创业理论和知识溢出理论,运用系统GMM方法,对2010-2014年全国32个省级城市的制造业新创企业的面板数据进行分析.研究结果表明:(1)新知识创造对本区域内和相邻区域的创业活动均有显著的促进作用;(2)新知识创造对本区域创业活动的促进作用要强于其对相邻区域创业活动的促进作用;(3)与非知识密集型产业相比,新知识创造对创业活动的促进作用在知识密集型产业中的效果更加明显.     

A Cox‐Aalen Model for Interval‐censored Data

The Cox‐Aalen model, obtained by replacing the baseline hazard function in the well‐known Cox model with a covariate‐dependent Aalen model, allows for both fixed and dynamic covariate effects. In this paper, we examine maximum likelihood estimation for a Cox‐Aalen model based on interval‐censored failure times with fixed covariates. The resulting estimator globally converges to the truth slower than the parametric rate, but its finite‐dimensional component is asymptotically efficient. Numerical studies show that estimation via a constrained Newton method performs well in terms of both finite sample properties and processing time for moderate‐to‐large samples with few covariates. We conclude with an application of the proposed methods to assess risk factors for disease progression in psoriatic arthritis.     

Estimation of the Jump Size Density in a Mixed Compound Poisson Process

In this paper, we consider a mixed compound Poisson process, that is, a random sum of independent and identically distributed (i.i.d.) random variables where the number of terms is a Poisson process with random intensity. We study nonparametric estimators of the jump density by specific deconvolution methods. Firstly, assuming that the random intensity has exponential distribution with unknown expectation, we propose two types of estimators based on the observation of an i.i.d. sample. Risks bounds and adaptive procedures are provided. Then, with no assumption on the distribution of the random intensity, we propose two non‐parametric estimators of the jump density based on the joint observation of the number of jumps and the random sum of jumps. Risks bounds are provided, leading to unusual rates for one of the two estimators. The methods are implemented and compared via simulations.     

Estimation of the Reliability of a Stress-Strength System from Power Lindley Distributions

In this paper, we are interested in the estimation of the reliability parameter R = P(X > Y) where X, a component strength, and Y, a component stress, are independent power Lindley random variables. The point and interval estimation of R, based on maximum likelihood, nonparametric and parametric bootstrap methods, are developed. The performance of the point estimate and confidence interval of R under the considered estimation methods is studied through extensive simulation. A numerical example, based on a real data, is presented to illustrate the proposed procedure.     

Robust confidence intervals for trend estimation in meta-analysis with publication bias

Confidence interval (CI) is very useful for trend estimation in meta-analysis. It provides a type of interval estimate of the regression slope as well as an indicator of the reliability of the estimate. Thus a precise calculation of confidence interval at an expected level is important. It is always difficult to explicitly quantify the CIs when there is publication bias in meta-analysis. Various CIs have been proposed, including the most widely used DerSimonian–Laird CI and the recently proposed Henmi–Copas CI. The latter provides a robust solution when there are non-ignorable missing data due to publication bias. In this paper we extended the idea into meta-analysis for trend estimation. We applied the method in different scenarios and showed that this type of CI is more robust than the others.