Experimental design and optimal decision i general remarks

The problems of an optimal selection of decision function, the design, and the sample size are worked out mainly in separate theories with different objective functions. In applications a unique objective function is given and with respect to this the three components of the statistical approach must be choosen simultaneously. The paper contents initial proposals for such an approach which is demonstrated by the example of parameter estimating in normal distribution. Besides a general theorem of separability and a theorem of duality of two optimization problems of the complete statistical problem are given.     

D s -optimal designs for Kozak's tree taper model

In this work, we study D _s -optimal design for Kozak's tree taper model. The approximate D _s -optimal designs are found invariant to tree size and hence create a ground to construct a general replication-free D _s -optimal design. Even though the designs are found not to be dependent on the parameter value p of the Kozak's model, they are sensitive to the s×1 subset parameter vector values of the model. The 12 points replication-free design (with 91% efficiency) suggested in this study is believed to reduce cost and time for data collection and more importantly to precisely estimate the subset parameters of interest.     

Multiple imputation in practice—a case study using a complex German establishment survey

Multiple imputation is widely accepted as the method of choice to address item nonresponse in surveys. Nowadays most statistical software packages include features to multiply impute missing values in a dataset. Nevertheless, the application to real data imposes many implementation problems. To define useful imputation models for a dataset that consists of categorical and possibly skewed continuous variables, contains skip patterns and all sorts of logical constraints is a challenging task. Besides, in most applications little attention is paid to the evaluation of the underlying assumptions behind the imputation models.     

Unconditional Quantile Treatment Effects Under Endogeneity

This article develops estimators for unconditional quantile treatment effects when the treatment selection is endogenous. We use an instrumental variable (IV) to solve for the endogeneity of the binary treatment variable. Identification is based on a monotonicity assumption in the treatment choice equation and is achieved without any functional form restriction. We propose a weighting estimator that is extremely simple to implement. This estimator is root n consistent, asymptotically normally distributed, and its variance attains the semiparametric efficiency bound. We also show that including covariates in the estimation is not only necessary for consistency when the IV is itself confounded but also for efficiency when the instrument is valid unconditionally. An application of the suggested methods to the effects of fertility on the family income distribution illustrates their usefulness. Supplementary materials for this article are available online.     

Measuring serial dependence in categorical time series

The analysis of time-indexed categorical data is important in many fields, e.g., in telecommunication network monitoring, manufacturing process control, ecology, etc. Primary interest is in detecting and measuring serial associations and dependencies in such data. For cardinal time series analysis, autocorrelation is a convenient and informative measure of serial association. Yet, for categorical time series analysis an analogous convenient measure and corresponding concepts of weak stationarity have not been provided. For two categorical variables, several ways of measuring association have been suggested. This paper reviews such measures and investigates their properties in a serial context. We discuss concepts of weak stationarity of a categorical time series, in particular of stationarity in association measures. Serial association and weak stationarity are studied in the class of discrete ARMA processes introduced by Jacobs and Lewis (J. Time Ser. Anal. 4(1):19–36, 1983). An intrinsic feature of a time series is that, typically, adjacent observations are dependent. The nature of this dependence among observations of a time series is of considerable practical interest. Time series analysis is concerned with techniques for the analysis of this dependence. (Box et al. 1994p. 1)     

Assessment of the Living Conditions in Polish and German Transborder Regions in the Context of Strengthening Territorial Cohesion in the European Union: Competitiveness or Complementation?

Social Indicators Research - The European Union's regional policy aims to strengthen economic, social, and territorial cohesion and equal space development opportunities. It is an action...     

Information measures and design issues in the study of mortality deceleration: findings for the gamma-Gompertz model

Lifetime Data Analysis - Mortality deceleration, or the slowing down of death rates at old ages, has been repeatedly investigated, but empirical studies of this phenomenon have produced mixed...     

To move or not to move to find a new job: spatial duration time model with dynamic covariate effects

The aim of this paper is to show the flexibility and capacity of penalized spline smoothing as estimation routine for modelling duration time data. We analyse the unemployment behaviour in Germany between 2000 and 2004 using a massive database from the German Federal Employment Agency. To investigate dynamic covariate effects and differences between competing job markets depending on the distance between former and recent working place, a functional duration time model with competing risks is used. It is build upon a competing hazard function where some of the smooth covariate effects are allowed to vary with unemployment duration. The focus of our analysis is on contrasting the spatial, economic and individual covariate effects of the competing job markets and on analysing their general influence on the unemployed's re-employment probabilities. As a result of our analyses, we reveal differences concerning gender, age and education. We also discover an effect between the newly formed and the old West German states. Moreover, the spatial pattern between the considered job markets differs.     

Rebound effects of progress in information technology

Information technology (IT) is continuously making astounding progress in technical efficiency. The time, space, material and energy needed to provide a unit of IT service have decreased by three orders of magnitude since the first personal computer (PC) was sold. However, it seems difficult for society to translate IT’s efficiency progress into progress in terms of individual, organizational or socio-economic goals. In particular it seems to be difficult for individuals to work more efficiently, for organizations to be more productive and for the socio-economic system to be more sustainable by using increasingly efficient IT. This article provides empirical evidence and potential explanations for this problem. Many counterproductive effects of IT can be explained economically by rebound effects. Beyond that, we conclude that the technological determinism adopted by decision-makers is the main obstacle in translating IT’s progress into non-technical goals.