Estimation of the intensity function of an inhomogeneous Poisson process with a change‐point

Recent work on point processes includes studying posterior convergence rates of estimating a continuous intensity function. In this article, convergence rates for estimating the intensity function and change‐point are derived for the more general case of a piecewise continuous intensity function. We study the problem of estimating the intensity function of an inhomogeneous Poisson process with a change‐point using non‐parametric Bayesian methods. An Markov Chain Monte Carlo (MCMC) algorithm is proposed to obtain estimates of the intensity function and the change‐point which is illustrated using simulation studies and applications. The Canadian Journal of Statistics 47: 604–618; 2019 © 2019 Statistical Society of Canada     

Parametrizing the Kepler exoplanet period-radius distribution with the bivariate normal inverse Gaussian distribution

This paper presents a simple and robust method for obtaining a comprehensive understanding of the joint period and radius distribution in Kepler exoplanets. The proposed method is based on particle swarm optimization and bivariate Normal Inverse Gaussian distribution. Furthermore, in the construction of the probability density function, this study selects planet-host stars with the GK-type. The injecting approach is also employed to solve the survey completeness of sample. The resulting occurrence rate of Earth analogs is 0.025 with a 95% bootstrap confidence interval between 0.023 and 0.032.     

The exponentiated power exponential regression model with different regression structures: application in nursing data

We define the exponentiated power exponential distribution and propose a regression model with different systematic structures based on the new distribution. We show that the new regression model can be applied to dispersion data since it represents a parametric family of models that includes as sub-models some widely-known regression models. It then can be used more effectively in the analysis of real data. We use maximum likelihood estimation and derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes. Some global-influence measurements are also investigated and simulation studies are performed to evaluate the accuracy of the estimates. We provide an application of the regression model with four systematic structures to nursing activities score data in the Unit of the Medical Clinic of University of São Paulo (USP) Hospital.     

Cox regression of clustered event times with covariates missing not at random

Motivated by a recent tuberculosis (TB) study, this paper is concerned with covariates missing not at random (MNAR) and models the potential intracluster correlation by a frailty. We consider the regression analysis of right‐censored event times from clustered subjects under a Cox proportional hazards frailty model and present the semiparametric maximum likelihood estimator (SPMLE) of the model parameters. An easy‐to‐implement pseudo‐SPMLE is then proposed to accommodate more realistic situations using readily available supplementary information on the missing covariates. Algorithms are provided to compute the estimators and their consistent variance estimators. We demonstrate that both the SPMLE and the pseudo‐SPMLE are consistent and asymptotically normal by the arguments based on the theory of modern empirical processes. The proposed approach is examined numerically via simulation and illustrated with an analysis of the motivating TB study data.     

The effect of small sample on optimal designs for logistic regression models

Asymptotic methods are commonly used in statistical inference for unknown parameters in binary data models. These methods are based on large sample theory, a condition which may be in conflict with small sample size and hence leads to poor results in the optimal designs theory. In this paper, we apply the second order expansions of the maximum likelihood estimator and derive a matrix formula for the mean square error (MSE) to obtain more precise optimal designs based on the MSE. Numerical results indicate the new optimal designs are more efficient than the optimal designs based on the information matrix.     

Modified profile likelihood estimation for the Weibull regression models in survival analysis

In this study, adjustment of profile likelihood function of parameter of interest in presence of many nuisance parameters is investigated for survival regression models. Our objective is to extend the Barndorff–Nielsen’s technique to Weibull regression models for estimation of shape parameter in presence of many nuisance and regression parameters. We conducted Monte-Carlo simulation studies and a real data analysis, all of which demonstrate and suggest that the modified profile likelihood estimators outperform the profile likelihood estimators in terms of three comparison criterion: mean squared errors, bias and standard errors.     

Cytometry inference through adaptive atomic deconvolution

In this paper, we consider a statistical estimation problem known as atomic deconvolution. Introduced in reliability, this model has a direct application when considering biological data produced by flow cytometers. From a statistical point of view, we aim at inferring the percentage of cells expressing the selected molecule and the probability distribution function associated with its fluorescence emission. We propose here an adaptive estimation procedure based on a previous deconvolution procedure introduced by Es, Gugushvili, and Spreij [(2008), ‘Deconvolution for an atomic distribution’, Electronic Journal of Statistics, 2, 265–297] and Gugushvili, Es, and Spreij [(2011), ‘Deconvolution for an atomic distribution: rates of convergence’, Journal of Nonparametric Statistics, 23, 1003–1029]. For both estimating the mixing parameter and the mixing density automatically, we use the Lepskii method based on the optimal choice of a bandwidth using a bias-variance decomposition. We then derive some convergence rates that are shown to be minimax optimal (up to some log terms) in Sobolev classes. Finally, we apply our algorithm on the simulated and real biological data.     

高校经管类学生参与科研意愿的影响因素分析——基于安徽省8所高校的问卷调查

大学生参与科研是高校培养双创型人才的重要途径之一,高校经管类专业学生培养存在重理论、轻实践的现象。安徽省8所高校1406名经济管理类学生科研意愿的实证分析表明:年级、学习成绩、科研重要性认知、科研了解程度和学校科研活动开展对大学生科研意愿有显著影响。高校在经管类学生培养过程中应通过加强科研宣传推广、开设科研训练课程、丰富科研活动多样性、激励教师科研指导和开拓校外实践教学基地建设等措施增强学生参与科研的积极性,提升学生的双创能力。     

企业经济责任投入对财务绩效影响的实证研究

将企业经济责任分为对股东的责任、对债权人的责任、对供应商的责任、对员工的责任,以此来设定经济责任投入的变量,分析研究其对企业财务绩效的影响。选取2011~2012年中国最受尊敬企业中在沪、深交易所上市的15家企业作为样本,根据各企业发布的相关财务数据,采用计量经济模型的方法,对财务绩效进行回归分析。结果显示：债权人、供应商、员工的责任与财务绩效均呈显著正向关系,说明企业经济责任的投人会促进财务绩效的提高。最后,提出促进企业财务绩效增长的对策建议。     

基于组合预测的风险值研究

针对准确地预测一个金融资产的风险值具有一定困难的问题,从风险值的特点出发,探讨了使用组合预测方法来预测风险值的意义以及确定组合预测权重和单个模型选取的方法。结论是用组合预测方法能提高风险值的预测表现;影响预测表现的关键因素是权重和单个模型的选取。