This article develops a hedonic price model to investigate empirically the structure of a rapidly evolving industrial market. The model postulates price distortions that arise out of intersegment and interfirm heterogeneity. The application of the model supplies evidence in support of the hypotheses. Empirical results are consistent with existing work on the role of technical performance and te...

In the process of cointegration analysis, electricity consumption is chosen as the explained variable, and GDP per capita, heavy industry share, and efficiency improvement are chosen as the explanatory variables; then a cointegration model is put forward, which shows that there is a cointegration relationship between the explained variable and explanatory variables. The explained and explanator...

The data base availability and the possibility to perform survey permit to the researcher to explore new segmentation model trying to involve methodological statistics and explanatory variables. Recent literature focused on values and lifestyles as predictors of consumer purchase (Kamakura, Mazzon, 1991). We purpose to build a survey based on questionnaire to analyze the influences of explanato...

In this article, we consider the nonparametric inference for time-varying coefficient double-threshold generalized autoregressive conditional heteroscedastic models. The quasi-maximum exponential likelihood estimators (QMELEs) of model’s parameters and asymptotic properties are obtained. simulation study implies that distribution is asymptotically normal. A real data application to stock return...

These data are from a controlled experiment in which two quantitative variables were manipulated, to determine their effects on the quantitative response variable. Both explanatory variables therefore are of direct interest; there are no supplemental variables and there is no need for reduction of variables. The study used a balanced factorial randomized design. Three levels of one explanatory ...

Data with a relatively small number of study individuals and a very large number of potential explanatory features arise particularly, but by no means only, in genomics. A powerful method of analysis, the lasso [Tibshirani R (1996) J Roy Stat Soc B 58:267-288], takes account of an assumed sparsity of effects, that is, that most of the features are nugatory. Standard criteria for model fitting, ...

in order to study the relationship between random boolean sets and some explanatory variables, this paper introduces a propagation model. this model can be applied when corresponding poisson process of the boolean model is related to explanatory variables and the random grains are not affected by these variables. an approximation for the likelihood is used to find pseudo-maximum likelihood esti...

This paper shows how to compute the Fisher information matrix and the asymptotic covariance matrix for maximum likelihood estimators for a wide class of parametric models that include combinations of censoring, truncation, and explanatory variables. Although the models are based on underlying location-scale distributions, applications extend, for example, directly to the closely related and wid...

Health services and health economics research articles commonly use multivariate regression techniques to measure the relationship of health service utilization and health outcomes (the outcomes of interest) with clinical characteristics, sociodemographic factors, and policy changes (usually treated as explanatory variables). Common regression methods measure differences in outcome variables be...