Generalized simultaneous confidence regions for regression coefficients and their ratios

This study constructs a simultaneous confidence region for two combinations of coefficients of linear models and their ratios based on the concept of generalized pivotal quantities. Many biological studies, such as those on genetics, assessment of drug effectiveness, and health economics, are interested in a comparison of several dose groups with a placebo group and the group ratios. The Bonferroni correction and the plug-in method based on the multivariate-t distribution have been proposed for the simultaneous region estimation. However, the two methods are asymptotic procedures, and their performance in finite sample sizes has not been thoroughly investigated. Based on the concept of generalized pivotal quantity, we propose a Bonferroni correction procedure and a generalized variable (GV) procedure to construct the simultaneous confidence regions. To address a genetic concern of the dominance ratio, we conduct a simulation study to empirically investigate the probability coverage and expected length of the methods for various combinations of sample sizes and values of the dominance ratio. The simulation results demonstrate that the simultaneous confidence region based on the GV procedure provides sufficient coverage probability and reasonable expected length. Thus, it can be recommended in practice. Numerical examples using published data sets illustrate the proposed methods.     

Back propagation neural networks and multiple regressions in the case of heteroskedasticity

This paper compares the performance between regression analysis and a clustering based neural network approach when the data deviates from the homoscedasticity assumption of regression. Heteroskedasticity is a problem that arises in linear regression due to the unequal error variances. One of the methods to deal heteroskedasticity in classical regression theory is weighted least-square regression (WLS). In order to deal the problem of heteroskedasticity, backpropagation neural network is applied. In this context, an algorithm is proposed which is based on robust estimates of location and dispersion matrix that helps in preserving the error assumption of the linear regression. Analysis is carried out with appropriate designs using simulated data and the results are presented.     

广西农业剩余劳动力转移状况研究

广西要想在西部率先全面建成小康社会。必须解决好农业剩余劳动力的转移问题。本文使用抽样法测算广西2008年农业剩余劳动力总数,并按照劳动力转移去向进行统计分析,揭示转移中存在的突出性问题和提出对策建议。     

大学生社会文化价值观特征分析

一个民族的文化通常有其系统性与适用的价值观,在当今中西文化交融的时代背景下,作为未来栋梁的大学生选择何种价值观体系,一直受到学术界的普遍关注。借鉴相关价值观研究理论,通过因子分析的方法,归纳出大学生的7个价值观类型,并揭示出大学生社会文化价值观的主要特征,即儒家思想中的进取精神得到了大学生的普遍认同,而极端功利与利己主义受到大学生的强烈排斥,大学生的"自我意识"呈现出不统一的特点,实用主义观念在大学生中广泛存在,传统价值观中的"节俭观"和"宿命论"在大学生中受到了严峻的挑战。     

MODELING THE ALLOCATION OF GRADUATE STUDENT FINANCIAL AID

Universities have always competed with one another for the relatively small number of highly qualified students interested in graduate education. This competition has been intensified recently by adverse demographic trends. This paper is concerned with the use of financial aid awards as a means of competition and with allocation of awards so that enrollments are maximized while satisfying academic standards and budget constraints. It also considers the randomness of actual aid disbursements and develops models of incremental aid allocation that increase enrollments but are within tolerance levels for budget overruns.     

STOCHASTIC COST-VOLUME-PROFIT ANALYSIS WITH A LINEAR DEMAND FUNCTION

In this paper we incorporate a linear demand function to model the price-volume causal relationship into stochastic cost-volume-profit (CVP) analysis. We assume that the objective function is to maximize expected profit; other objective functions are also discussed and compared. A linear stochastic model follows from which probabilistic statements can be easily obtained if the random variables are assumed to be multivariate normal. The basic framework is shown to be a special case of project value maximization where project value is the cash flow of the project discounted for time and risk according to the capital asset pricing model. Moreover, an intertemporal extension that considers inventory is developed. In summary, a new approach to stochastic CVP analysis that incorporates the management decision process in an uncertain environment is developed.     

Notes and Communications “A DECISION THEORY EXAMPLE IN FOOTBALL”: A COMMENT*

Janssen and Daniel analyzed the choice between a one- or a two-point conversion for a particular game situation in college football. Their decision criteria was maximum expected utility based on a von Neumann-Morgenstern utility function defined over the games outcomes. An alternative approach based on a stochastic dominance criterion is presented that does not rely on knowledge of the relative importance of tying vs. winning; rather, it relies on a notion of consistency in the sequential problem.     

A NOTE ON HOLDING COSTS AND LOT-SIZE ERRORS

This note examines the sensitivity of the basic economic-order-quantity inventory model to lot-size errors when holding costs are assumed to be a strictly increasing (though not necessarily linear) function of average inventory. In particular, we show that the penalty associated with ordering either too much or too little is a function not only of the size of the error but of the shape of the holding-cost curve as well. We demonstrate that, under certain conditions, even relatively small lot-size errors can be extremely costly.     

Concepts,Theory, and Techniques PRINCIPLES OF MULTIOBJECTIVE OPTIMIZATION*

This paper attempts to isolate and analyze the principal ideas of multiobjective optimization. We do this without casting aspersions on single-objective optimization or championing any one multiobjective technique. We examine each fundamental idea for strengths and weaknesses and subject two—efficiency and utility—to extended consideration. Some general recommendations are made in light of this analysis. Besides the simple advice to retain single-objective optimization as a possible approach, we suggest that three broad classes of multiobjective techniques are very promising in terms of reliably, and believably, achieving a most preferred solution. These are: (1) partial generation of the efficient set, a rubric we use to unify a wide spectrum of both interactive and analytic methods; (2) explicit utility maximization, a much-overlooked approach combining multiattribute decision theory and mathematical programming; and (3) interactive implicit utility maximization, the popular class of methods introduced by Geoffrion, Dyer, and Feinberg [24] and extended significantly by others.     

Determining Sample Size Using Fast and Slow Thinking

ABSTRACT

Using SPSS's bootstrapping procedures, this article demonstrates an approach to determining sample size that combines fast (heuristics or rules-of-thumb) and slow (statistical power analysis) thinking to balance statistical power, precision, and practicality. Sample size is determined for six commonly used statistical procedures: independent groups t-test, one-way ANOVA, one-way MANOVA, Pearson's r correlation, linear regression, and logistic regression. Overall, findings suggest that both approaches may under or over-estimate sample size. Both approaches yielded similar parameter and confidence interval estimates, but varied, sometimes by a factor of two, in their sample size requirements. It is hoped that this study's procedure and results will provide beginning reference points for sample size determination, and encourage researchers continue to search for resolutions for often difficult sample-size decisions.