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Optimal Scaling (optimal + scaling)

Distribution by Scientific Domains
Mathematics and Statistics75%

Selected Abstracts

Alcohol use and abuse in adolescence: proposal of an alternative analysis

CHILD: CARE, HEALTH AND DEVELOPMENT, Issue 3 2008
C. Simões
Abstract Background A national, representative, school-based sample of Portuguese youths was used to examine the prevalence of alcohol use in this population and to analyse differences between demographic variables such as gender and age, as well as to propose a statistical procedure that optimally quantifies categorical variables. Methods Data on 6109 state school students from Portugal, in the 6th, 8th and 10th grades, aged 11,18, who participated in the 2002 (Health Behaviour in School-aged Children/WHO) survey of adolescent health, were analysed. Adolescents aged between 11 and 14 were placed in the younger group, and those 15,18 years old were placed in the older group. Optimal scaling was used to optimize the computation of factor scores, which were subsequently submitted to multiple regression analysis in order to analyse the impact of gender and age on alcohol use. Results The results of this study show that the majority of Portuguese school-aged adolescents attending regular school at 6th, 8th and 10th grades do not drink alcoholic beverages (beer, wine or spirits) on a regular basis (at least once a month). However, about 8% of these adolescents do drink beer, 3% do drink wine and 12% do drink spirits on a regular basis. With regard to age and gender, about a quarter of the older boys stated that they drink beer or spirits regularly. The multiple regression analysis showed that age and gender had a significant impact on alcohol use. Conclusion Alcohol , in particular spirits , is a substance used by some Portuguese adolescents. Alcohol use and abuse is more frequent in boys and increases with age. The importance of these findings for health promotion strategies is discussed. [source]

Mining performance data through nonlinear PCA with optimal scaling

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 1 2010
Paola Costantini
Abstract Performance data are usually collected in order to build well-defined performance indicators. Since such data may conceal additional information, which can be revealed by secondary analysis, we believe that mining of performance data may be fruitful. We also note that performance databases usually contain both qualitative and quantitative variables for which it may be inappropriate to assume some specific (multivariate) underlying distribution. Thus, a suitable technique to deal with these issues should be adopted. In this work, we consider nonlinear principal component analysis (PCA) with optimal scaling, a method developed to incorporate all types of variables, and to discover and handle nonlinear relationships. The reader is offered a case study in which a student opinion database is mined. Though generally gathered to provide evidence of teaching ability, they are exploited here to provide a more general performance evaluation tool for those in charge of managing universities. We show how nonlinear PCA with optimal scaling applied to student opinion data enables users to point out some strengths and weaknesses of educational programs and services within a university. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Acoustic limit for the Boltzmann equation in optimal scaling

COMMUNICATIONS ON PURE & APPLIED MATHEMATICS, Issue 3 2010
Yan Guo
Based on a recent L2 , L, framework, we establish the acoustic limit of the Boltzmann equation for general collision kernels. The scaling of the fluctuations with respect to the Knudsen number is optimal. Our approach is based on a new analysis of the compressible Euler limit of the Boltzmann equation, as well as refined estimates of Euler and acoustic solutions. © 2009 Wiley Periodicals, Inc. [source]