Performance of the stein-type two-parameter estimator in multiple linear regression model

In this article, we consider the Stein-type approach to the estimation of the regression parameter in a multiple regression model under a multicollinearity situation. The Stein-type two-parameter estimator is proposed when it is suspected that the regression parameter may be restricted to a subspace. The bias and the quadratic risk of the proposed estimator are derived and compared with the two-parameter estimator (TPE), the restricted TPE and the preliminary test TPE. The conditions of superiority of the proposed estimator are obtained. Finally, a real data example is provided to illustrate some of the theoretical results.     

Comparisons of several Pareto distributions based on record values

In order to avoid wrong conclusions in any further analysis, it is of importance to conduct a formal comparison for characteristic quantities of the distributions. These characteristic quantities we are familiar with include mean, quantity and reliability function, and so on. In this paper, we consider two tests aiming at the comparisons for function of parameters in Pareto distribution based on record values. They are generalized p-value-based test and parametric bootstrap-based test, respectively. The resulting procedures are easy to compute and are applicable to small samples. A simulation study is conducted to investigate and compare the performance of the proposed tests. A phenomenon we note is that generalized p-value-based test almost uniformly outperforms the parametric bootstrap-based test.     

Performance of the difference-based Liu-type estimator in partially linear model

This paper discusses the parameter estimation in a partially linear model. We proposed a difference-based Liu-type estimator of the unknown parameters in the partially linear model. The asymptotically properties of the proposed estimator are discussed. We propose a iterative method to choose the biasing parameters. Finally, a simulation study and a numerical example are presented to explain the performance of the estimators.     

Strong and weak consistency of LS estimators in the EV regression model with negatively superadditive-dependent errors

In this paper, the strong laws of large numbers for partial sums and weighted sums of negatively superadditive-dependent (NSD, in short) random variables are presented, especially the Marcinkiewicz–Zygmund type strong law of large numbers. Using these strong laws of large numbers, we further investigate the strong consistency and weak consistency of the LS estimators in the EV regression model with NSD errors, which generalize and improve the corresponding ones for negatively associated random variables. Finally, a simulation is carried out to study the numerical performance of the strong consistency result that we established.     

The Development and Initial Validation of a Psychoanalysis Use/Non-Use Scale among a Sample of Hong Kong Helping Professionals.

ABSTRACT

This article reports on the construction and initial validation of the Psychoanalysis Use/Non-Use scale (PUNU) and the complementary Conflicts with Hong Kong Chinese Culture scale (CHKCC), developed for assessing the penetration of psychoanalysis among counseling professionals in Hong Kong. A total of 217 Hong Kong social workers, psychologists, and counselors were surveyed to assess the scales' internal psychometric properties. Factor analysis confirmed a four-factor structure for the PUNU, and a unidimensional structure for the CHKCC, with 17 and 9 items, respectively. Internal reliability of both scales was good and discriminative validity was supported by comparisons with background information. These scales could help to identify factors that affect the understanding of psychoanalysis among professionals and could be useful for educational purposes.     

《老子》“道者万物之主也”古今注译辨析

《老子》第62章提出了“道者万物之主也”的主张,古今注家轻忽老子思想本质及《老子》文本实际,多将其解为“万物被道宰制”或“道是万物的主宰”。实际上,老子是反斥外力主宰万物的,《老子》文中也未有道宰制万物的语义表述。验之《老子》,“主”于修辞角度当用其本义（“微弱的灯火”）,而于语义视角则应用其与“助”、“辅”共通之义（“相依相佐”）,也用其与“职”、“掌”同源之义（“主动认知而作为”）,而不应释为其引伸义（“主宰”或“宰制”）。“道者万物之主也”要义为：老子于乱世中将道比作黑暗中微弱的灯火,坚信唯有道才能真正辅益万物生发成长,助力乱世返璞归真,而在此过程中起主导或主宰作用的是万物或世人,而非工具或境界性质之道。这体现了老子以人为本,崇尚自然,信仰唯有主动皈依自身本真才是万物或乱世之拯救正途的济世情怀。     

Labor Market Segmentation,Job Mobility and the Two-track Model of Chinese Urban Workers＇ Acquisition of Economic Status

使用“2009年中国社会网络与职业经历调查”（JSNET2009）广州、上海、厦门、济南和西安五城市数据,检验了改革以后影响中国城市劳动者职业流动模式以及经济地位获得的因素。研究结果表明,高学历劳动者与低学历劳动者群体处于两个分割的劳动力市场中,他们的经济地位获得路径完全不同。对于低学历劳动者,职业流动是提升他们收入水平的最重要因素,而人力资本因素（受教育年限和工作经验）对他们的收入没有影响。高学历劳动者的情况刚好相反,职业流动对收入获得没有任何作用,影响他们收入分层的最重要因素是人力资本。本研究揭示了转型期中国城市地区不同劳动力市场劳动者的经济地位获得的二元路径模式。     

扭带管内油的受迫对流换热实验

以30~#透平油为工质,对在光管内插入三种不同扭率扭带的受迫流动的冷、热态流阻及换热性能进行了实验。实验的雷诺数为200～1150,普朗特数为320～590,实验结果表明:在本实验范围内,插有不同扭率的全长连续扭带管的换热系数为光管的1.7～2.6倍,而阻力系数为光管的3～7.5倍;且扭率的大小对扭带的流阻与换热影响较大。另外,还对间断扭带管内油的受迫对流换热进行了实验。     

A Bayesian approach for generalized linear models with explanatory biomarker measurement variables subject to detection limit: an application to acute lung injury

Biomarkers have the potential to improve our understanding of disease diagnosis and prognosis. Biomarker levels that fall below the assay detection limits (DLs), however, compromise the application of biomarkers in research and practice. Most existing methods to handle non-detects focus on a scenario in which the response variable is subject to the DL; only a few methods consider explanatory variables when dealing with DLs. We propose a Bayesian approach for generalized linear models with explanatory variables subject to lower, upper, or interval DLs. In simulation studies, we compared the proposed Bayesian approach to four commonly used methods in a logistic regression model with explanatory variable measurements subject to the DL. We also applied the Bayesian approach and other four methods in a real study, in which a panel of cytokine biomarkers was studied for their association with acute lung injury (ALI). We found that IL8 was associated with a moderate increase in risk for ALI in the model based on the proposed Bayesian approach.     

Covariate-defined latent space random effects model

Latent factor models are a useful and intuitive class of models; one limitation is their inability to predict links in a dynamic network. We propose a latent space random effects model with a covariate-defined social space, where the social space is a linear combination of the covariates as estimated by an MCMC algorithm. The model allows for the prediction of links in a network; it also provides an interpretable framework to explain why people connect. We fit the model using the Adolescent Health Network dataset and three simulated networks to illustrate its effectiveness in recognizing patterns in the data.