Using local linear kernel smoothers to test the lack of fit of nonlinear regression models

Herein, we propose a data-driven test that assesses the lack of fit of nonlinear regression models. The comparison of local linear kernel and parametric fits is the basis of this test, and specific boundary-corrected kernels are not needed at the boundary when local linear fitting is used. Under the parametric null model, the asymptotically optimal bandwidth can be used for bandwidth selection. This selection method leads to the data-driven test that has a limiting normal distribution under the null hypothesis and is consistent against any fixed alternative. The finite-sample property of the proposed data-driven test is illustrated, and the power of the test is compared with that of some existing tests via simulation studies. We illustrate the practicality of the proposed test by using two data sets.     

Merging information for semiparametric density estimation

Summary.  The density ratio model specifies that the likelihood ratio of m −1 probability density functions with respect to the m th is of known parametric form without reference to any parametric model. We study the semiparametric inference problem that is related to the density ratio model by appealing to the methodology of empirical likelihood. The combined data from all the samples leads to more efficient kernel density estimators for the unknown distributions. We adopt variants of well-established techniques to choose the smoothing parameter for the density estimators proposed.     

Sifting the wheat from the chaff—the organization of telephone intake and the selection of social assistance inquirers in Sweden

In Sweden, the initial contact between help-seeking people and a social welfare office normally takes place over the telephone, with designated intake officers handling those aiming to apply for social assistance. Citizens seeking help are here sorted according to various eligibility criteria as well as according to different intake routines. The outcome of the sorting process is that some receive an appointment for a further assessment while others are sent away. This article describes and classifies the degree of selection at seven telephone intakes, discusses relations between the degrees of selection and reasons for selection and distinguishes several selection strategies.     

On the shrinkage of local linear curve estimators

Local linear curve estimators are typically constructed using a compactly supported kernel, which minimizes edge effects and (in the case of the Epanechnikov kernel) optimizes asymptotic performance in a mean square sense. The use of compactly supported kernels can produce numerical problems, however. A common remedy is ridging, which may be viewed as shrinkage of the local linear estimator towards the origin. In this paper we propose a general form of shrinkage, and suggest that, in practice, shrinkage be towards a proper curve estimator. For the latter we propose a local linear estimator based on an infinitely supported kernel. This approach is resistant against selection of too large a shrinkage parameter, which can impair performance when shrinkage is towards the origin. It also removes problems of numerical instability resulting from using a compactly supported kernel, and enjoys very good mean squared error properties.     

Testing for linearity in generalized linear models using kernel smoothing

A test statistic proposed by Li (1999) for testing the adequacy of heteroscedastic nonlinear regression models using nonparametric kernel smoothers is applied to testing for linearity in generalized linear models. Simulation results for models with centered gamma and inverse Gaussian errors are presented to illustrate the performance of the resulting test compared with log-likelihood ratio tests for specific parametric alternatives. The test is applied to a data set of coronary heart disease status (Hosmer and Lemeshow, (1990).     

Evaluation of scheduling strategies for a dynamic job shop in a tool-shared,flexible manufacturing environment

A two-phase approach is used to examine the impact of job scheduling rules and tool selection policies for a dynamic job shop system in a tool-shared, flexible manufacturing environment. The first phase develops a generalized simulation model and analyses 'simple' job scheduling rules and tool selection policies under various operating scenarios. The results from this investigation are then used to develop and analyse various bi-criteria rules in the second phase of this study. The results show that the scheduling rules have the most significant impact on system performance, particularly at high shop load levels. Tool selection policies affect some of the performance measures, most notably, proportion of tardy jobs, to a lesser degree. Higher machine utilizations can be obtained at higher tool duplication levels but at the expense of increased tooling costs and lower tool utilization. The results also show that using different processing time distributions may have a significant impact on shop performance.     

A decision support system for the selection of a casting process using analytic hierarchy process

This paper presents an Analytic Hierarchy Process (AHP) based decision support system to select the most suitable casting process for a given product. The hierarchical structure of the proposed method allows the decision maker to compare the different casting processes using the material suitability and flexibility, geometrical complexity, dimensional tolerance and surface finish of the casting, and the cost as the criteria for selection. Judgemental inconsistency of the decision maker in selecting the casting process is taken care by ensuring that the value of consistency ratio is below (0.1). A numerical example is presented to illustrate the effectiveness of the proposed methodology for selecting the suitable casting process.     

The recruitment and selection of hospital medical consultants

Abstract

The literature on selection has been dominated in recent decades by the ‘prediction’ paradigm. Such an approach requires a substantial number of positions to be filled in order to compute validity coefficients. There are other approaches, using a different paradigm, when concentrated on adjusting the organization and the employee. Here careful analysis of job demands is essential. This article describes such an analysis, taking the medical consultant as an example. The method can be used in an iterative way. It is useful when one has to work with small numbers of positions.     

样本选择参数分位回归模型及其在工资分布分解中的应用

在工资差距分解问题中,研究者经常会遇到样本选择偏差问题,直接忽略会导致最终估计结果产生严重偏差,同时在众多工资差距分解方法中,相比于均值分解,分布分解方法更受研究者青睐。针对参数分位回归,本文首次提出可加形式与非可加形式的样本选择参数分位回归（SSPQR）模型,并基于这两类样本选择参数分位回归模型给出修正样本选择偏差后的参数分位回归工资差距分布分解方法。运用上述方法及已有的工资分布分解方法,借助CHNS2015年度城镇数据,本文研究了我国城镇男女工资差距及差距分解问题,得出以下结论：①男女工资差距主要来源是性别歧视问题;②经过样本选择偏差修正后,实际的工资差距更大,歧视问题更严重;③男女工资差距程度在不同分位点上结果不同,换句话说,我们不能简单地仅从平均水平来判断工资差距程度;④与其他已有方法计算结果比较发现,SSPQR计算的工资差距程度更大。     

中国省际人口流动与 人力资本流动差异性分析

人力资本流动与人口流动相关却不相同,本文采用2010-2016年全国流动人口动态监测数据,从流动方向和流动强度两方面对我国省际人口流动引致的人力资本流动现象加以研究。结果表明从流动方向分析,各省人口净流动方向基本呈现时间一致性并且流入流出区域具有稳定性。在调查期间人口净流出省份为16个,人口净流入省份为15个,没有明显变化。人力资本净流向与人口净流向完全一致,但省际人口净流向与高级人力资本净流向并不完全一致,山西、广西、重庆等省市的高级人力资本净流向始终与总人力资本净流向相反。本文采用流出流入比率和迁移选择中心两种指标测算人力资本流动强度,结果基本一致。人力资本流动中心与人口流动中心分布均较为固定。2010年人力资本流动强度最大的省份在2016年的流动强度也更强,流动强度在省际呈现惯性和马太效应。人力资本流入最强地区包括北京、天津、上海三个直辖市,人力资本流出最强省份始终包括安徽、四川、河南等省份。以人力资本流出弹性衡量人力资本流出强度与人口流出强度的相对强弱,东部地区、东北地区省份人力资本的流出强度始终大于人口流出强度,西部地区省份差异较大。考虑各省近五年经济增长状况,人口流动或者人力资本流动对经济增长的影响是非线性的。