Das 360-Grad-Feedback zur Unterstützung der Kompetenzerweiterung von Führungskräften

Zusammenfassung:  Der Beitrag beschreibt die Einbettung des 360-Grad-Feedbacks in ein Programm der Führungskr?fteentwicklung. 360-Grad-Feedback ist eine Voraussetzung für die systematische Kompetenzentwicklung der Führungskr?fte eines Unternehmens. Darüber hinaus k?nnen die Entwicklungs- und Lernm?glichkeiten, die ein individuelles Coaching bietet, durch ein vorheriges 360-Grad-Feedback optimiert werden. Die bei ThyssenKrupp Services mit dem F?rderprogramm „best-for-best“ gesammelten Erfahrungen zeigen deutlich, dass die Kombination von einem 360-Grad-Feedback mit gezielten Coaching-Angeboten ein echtes Erfolgsmodell darstellt.

清末民国石印本《杨家将》鼓词综述

依据所收藏清末民国石印本《杨家将》鼓词14种,它们是清代鼓词文献的重要组成部分。从源流和流通方式来说,《杨家将》鼓词经历了从抄本、坊刻本到石印本、铅印本的演变过程,保存了传统民间说唱文学的珍贵资料,包括民间说唱的原始风貌、大量俗字、别字和方言词汇,对于传统说唱文学、小说和戏剧、文字学乃至民俗、出版史和绘画史研究都具有很高的文献研究价值。     

Uncertainty Quantification in Case of Imperfect Models: A Non‐Bayesian Approach

The starting point in uncertainty quantification is a stochastic model, which is fitted to a technical system in a suitable way, and prediction of uncertainty is carried out within this stochastic model. In any application, such a model will not be perfect, so any uncertainty quantification from such a model has to take into account the inadequacy of the model. In this paper, we rigorously show how the observed data of the technical system can be used to build a conservative non‐asymptotic confidence interval on quantiles related to experiments with the technical system. The construction of this confidence interval is based on concentration inequalities and order statistics. An asymptotic bound on the length of this confidence interval is presented. Here we assume that engineers use more and more of their knowledge to build models with order of errors bounded by . The results are illustrated by applying the newly proposed approach to real and simulated data.     

Bayesian methods for dealing with missing data problems

Missing data, a common but challenging issue in most studies, may lead to biased and inefficient inferences if handled inappropriately. As a natural and powerful way for dealing with missing data, Bayesian approach has received much attention in the literature. This paper reviews the recent developments and applications of Bayesian methods for dealing with ignorable and non-ignorable missing data. We firstly introduce missing data mechanisms and Bayesian framework for dealing with missing data, and then introduce missing data models under ignorable and non-ignorable missing data circumstances based on the literature. After that, important issues of Bayesian inference, including prior construction, posterior computation, model comparison and sensitivity analysis, are discussed. Finally, several future issues that deserve further research are summarized and concluded.     

A bivariate distribution with Lomax and geometric margins

We develop a stochastic model describing the joint distribution of $(X,N)$, where $N$ has a geometric distribution while $X$ is the sum of $N$ dependent, heavy-tail Pareto components. Models of this form arise in many applications, ranging from hydro-climatology to finance and insurance. We present fundamental properties of this vector, including marginal and conditional distributions, moments, representations, and parameter estimation. We also include an example from finance, illustrating modeling potential of this new bivariate distribution.     

Parametric inference based on judgment post stratified samples

In this paper, we consider a judgment post stratified (JPS) sample of set size $H$ from a location and scale family of distributions. In a JPS sample, ranks of measured units are random variables. By conditioning on these ranks, we derive the maximum likelihood (MLEs) and best linear unbiased estimators (BLUEs) of the location and scale parameters. Since ranks are random variables, by considering the conditional distributions of ranks given the measured observations we construct Rao-Blackwellized version of MLEs and BLUEs. We show that Rao-Blackwellized estimators always have smaller mean squared errors than MLEs and BLUEs in a JPS sample. In addition, the paper provides empirical evidence for the efficiency of the proposed estimators through a series of Monte Carlo simulations.     

China's energy inefficiency: A cross-country comparison

This paper constructs a total-factor energy technical efficiency index using the data envelopment analysis (DEA) method following the total factor productivity framework. We then compare energy technical efficiency across 156 countries from 1980 to 2007. The results show that China's energy efficiency considerably trails other countries’ although it has made significant gains within the last 28 years. Further analysis indicates that scale inefficiency rather than pure technical efficiency contributes to China's energy inefficiency.     

The size and structure of China's government debt

This paper analyzes the size and structure of China's government debt. In addition to explicit government debt, we consider three types of government contingent liabilities: local government debt, university debt, and state banks’ nonperforming loans. The size of each types of debt is estimated and the reasons for the emergence of each type of debt are analyzed. International comparisons are made and it is found that China's government debt is larger than many other developing countries. To insure fiscal sustainability and to leave rooms for future expansionary fiscal policies, the government should reduce contingent liabilities.