Combining Stated and Revealed Preference Data to Construct an Empirical Examination of Intrahousehold Bargaining

Most analysis of intrahousehold decisions has been conducted using aggregate revealed preference data where preferences of individual agents are never observed, only the household consumption outcomes are observed. We examine household behavior in a bargaining framework by combining stated preference information from individual members of the household with revealed preference information on the household’s actual choices. The resulting model provides information on the degree to which the decision is the result of a bargaining process as well as information on the factors affecting bargaining power.

The Application of Family Systems Theory to Geriatric Hospital Social Work

Integrating and applying Family Systems Theory principles into the assessment and treatment process of families of hospitalized high risk elderly is an area that has received little attention in health care literature. The multigenerational approach is a model that can be used as a clinical framework for guiding the social workers in developing intervention strategies when working with hospitalized high risk elderly and their families. This approach can be extended to clinical assessment techniques and psychosocial rationales for discharge planning. Three case examples are presented to illustrate how Family Systems Theory can be integrated into social work practice resulting in effective interventions for hospitalized high risk elderly and their families.     

A comparison of methods for constructing confidence intervals on contrasts in a three-factor mixed model

This paper compares several methods for constructing a confidence interval on contrasts of fixed effects in a balanced three-factor mixed factorial design with one fixed effect and two random effects. In particular, confidence intervals constructed using PROC MIXED of SAS are compared to other intervals that have been proposed in the literature. Computer simulation is used to compare interval lengths, and determine each method's ability to maintain the stated confidence coefficient.     

Modelling claim number using a new mixture model: negative binomial gamma distribution

ABSTRACT

In actuarial applications, mixed Poisson distributions are widely used for modelling claim counts as observed data on the number of claims often exhibit a variance noticeably exceeding the mean. In this study, a new claim number distribution is obtained by mixing negative binomial parameter p which is reparameterized as p?=?exp( ?λ) with Gamma distribution. Basic properties of this new distribution are given. Maximum likelihood estimators of the parameters are calculated using the Newton–Raphson and genetic algorithm (GA). We compared the performance of these methods in terms of efficiency by simulation. A numerical example is provided.     

An iterative sparse algorithm for the penalized maximum likelihood estimator in mixed effects model

In this paper, we propose a new iterative sparse algorithm (ISA) to compute the maximum likelihood estimator (MLE) or penalized MLE of the mixed effects model. The sparse approximation based on the arrow-head (A-H) matrix is one solution which is popularly used in practice. The A-H method provides an easy computation of the inverse of the Hessian matrix and is computationally efficient. However, it often has non-negligible error in approximating the inverse of the Hessian matrix and in the estimation. Unlike the A-H method, in the ISA, the sparse approximation is applied “iteratively” to reduce the approximation error at each Newton Raphson step. The advantages of the ISA over the exact and A-H method are illustrated using several synthetic and real examples.     

家庭早期读写训练与儿童读写成绩间的关系研究

利用儿童家庭一学校早期语言和识字能力系列测验对每个目标儿童语言与识字能力发展进行了评定，并着重揭示了这些测量结果与儿童家庭语言、识字测量指标之间关系。结果表明：(1)家庭早期启发性谈话可以预测儿童完成定义任务和对熊故事进行建构的程度；(2)吃饭期间说明性和解释性谈话可预测儿童词汇量的发展状况和理解力；(3)书本阅读可预测儿童识字应变能力及对故事的理解和建构能力；(4)儿童早期玩具游戏与后来读写成绩之间无关。     

Nonnegative unbiased estimability of linear combinations of two variance components

For a general mixed model with two variance components θ₁ and θ₂, a criterion for a function q₁θ₁+q₂θ₂ to admit an unbiased nonnegative definite quadratic estimator is established in a form that allows answering the question of existence of such an estimator more explicitly than with the use of the criteria known hitherto. An application of this result to the case of a random one-way model shows that for many unbalanced models the estimability criterion is expressible directly by the largest of the numbers of observations within levels, thus extending the criterion established by LaMotte (1973) for balanced models.     

Strong-mixed searching and pathwidth

In this paper, we propose a new search model, called strong-mixed search, which is a generalization of the mixed search. We show that the strong-mixed search number of a graph equals the pathwidth of the graph. We also describe relationships between the strong-mixed search number and other search numbers.Research was supported in part by NSERC and MITACS.     

Attribute-variable Inspection Policy for Lots Using Resampling Based on EWMA

A new mixed sampling plan which is a combination of the attribute single sampling plan and variables resampling scheme based on EWMA statistic is proposed in this paper. The operating characteristic function of the proposed plan is derived and the plan parameters are determined such that the probability of acceptance of good lot is larger than the specified producer's confidence level and the bad lot acceptance probability is smaller than the consumer's confidence level. The efficiency and the advantages of the proposed plan are discussed over the existing attribute sampling plan. The extensive tables are provided for industrial applications. The use of tables is discussed with the help of a real-time industrial example.     

Sufficient dimension reduction through informative predictor subspace

The purpose of this paper is to define the central informative predictor subspace to contain the central subspace and to develop methods for estimating the former subspace. Potential advantages of the proposed methods are no requirements of linearity, constant variance and coverage conditions in methodological developments. Therefore, the central informative predictor subspace gives us the benefit of restoring the central subspace exhaustively despite failing the conditions. Numerical studies confirm the theories, and real data analyses are presented.