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31.
Bengt Sisteen jur.pol.mag. 《Endeavour》1979,3(4):158-162
That technological developments are strongly influenced by economic, political, and social factors is now widely recognised. The interplay of these factors is well exemplified by Sweden's system of storing strategic reserves of oil underground in huge caverns blasted from solid rock. With the completion of present plans total capacity of peace-time reserves will exceed ten million cubic metres. 相似文献
32.
E. S. Tidningarnas Telegrambyrå Mats Knutson Jacob Sundberg Anki Gundhäll Professor Lars Gustafsson Alan Dershowitz Svante Nycander Bengt Johansson Magnus Eriksson Lotta Gustavson Marianne Gunnarsson Kristina Vallström Monique Wadsted Mary Ann Glendon Professor Gerhard Radnitzky Jescheck Anders Victorin Johan åsard Lars Isaksson 《Minerva》1991,29(3):321-385
33.
AbstractFactor mixture models are designed for the analysis of multivariate data obtained from a population consisting of distinct latent classes. A common factor model is assumed to hold within each of the latent classes. Factor mixture modeling involves obtaining estimates of the model parameters, and may also be used to assign subjects to their most likely latent class. This simulation study investigates aspects of model performance such as parameter coverage and correct class membership assignment and focuses on covariate effects, model size, and class-specific versus class-invariant parameters. When fitting true models, parameter coverage is good for most parameters even for the smallest class separation investigated in this study (0.5 SD between 2 classes). The same holds for convergence rates. Correct class assignment is unsatisfactory for the small class separation without covariates, but improves dramatically with increasing separation, covariate effects, or both. Model performance is not influenced by the differences in model size investigated here. Class-specific parameters may improve some aspects of model performance but negatively affect other aspects. 相似文献
34.
This article examines the problem of specification error in 2 models for categorical latent variables; the latent class model and the latent Markov model. Specification error in the latent class model focuses on the impact of incorrectly specifying the number of latent classes of the categorical latent variable on measures of model adequacy as well as sample reallocation to latent classes. The results show that the clarity of remaining latent classes, as measured by the entropy statistic depends on the number of observations in the omitted latent class—but this statistic is not reliable. Specification error in the latent Markov model focuses on the transition probabilities when a longitudinal Guttman process is incorrectly specified. The findings show that specifying a longitudinal Guttman process that is not true in the population impacts other transition probabilities through the covariance matrix of the logit parameters used to calculate those probabilities. 相似文献