Interval Estimation for the Correlation Coefficient

ABSTRACT

The correlation coefficient (CC) is a standard measure of a possible linear association between two continuous random variables. The CC plays a significant role in many scientific disciplines. For a bivariate normal distribution, there are many types of confidence intervals for the CC, such as z-transformation and maximum likelihood-based intervals. However, when the underlying bivariate distribution is unknown, the construction of confidence intervals for the CC is not well-developed. In this paper, we discuss various interval estimation methods for the CC. We propose a generalized confidence interval for the CC when the underlying bivariate distribution is a normal distribution, and two empirical likelihood-based intervals for the CC when the underlying bivariate distribution is unknown. We also conduct extensive simulation studies to compare the new intervals with existing intervals in terms of coverage probability and interval length. Finally, two real examples are used to demonstrate the application of the proposed methods.     

Review: Reversed low-rank ANOVA model for transforming high dimensional genetic data into low dimension

A general modeling procedure for analyzing genetic data is reviewed. We review ANOVA type model that can handle both the continuous and discrete genetic variables in one modeling framework. Unlike the regression type models which typically set the phenotype variable as a response, this ANOVA model treats the phenotype variable as an explanatory variable. By reversely treating the phenotype variable, usual high dimensional problem is turned into low dimension. Instead, the ANOVA model always includes interaction term between the genetic locations and phenotype variable to find potential association between them. The interaction term is designed to be low rank with the multiplication of bilinear terms so that the required number of parameters is kept in a manageable degree. We compare the performance of the reviewed ANOVA model to the other popular methods via microarray and SNP data sets.     

What Makes Association Members Donate More? Factors Influencing Members’ Donation Amount in Membership-Based Professional Associations

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - As charitable donations account for a significant amount of the revenue of professional associations, such associations...     

Analysis of Survival Data with Group Lasso

Identification of influential genes and clinical covariates on the survival of patients is crucial because it can lead us to better understanding of underlying mechanism of diseases and better prediction models. Most of variable selection methods in penalized Cox models cannot deal properly with categorical variables such as gender and family history. The group lasso penalty can combine clinical and genomic covariates effectively. In this article, we introduce an optimization algorithm for Cox regression with group lasso penalty. We compare our method with other methods on simulated and real microarray data sets.     

Score Tests for Testing Independence in the Zero-Truncated Bivariate Poisson Models

In this study, score test statistics for testing independence in the zero-truncated bivariate Poisson distributions are proposed. The Monte Carlo study shows that the score tests proposed in this article keep the significance level close to the nominal one, but the LR and Wald tests over-reject the null hypothesis when it is true. The score tests for testing independence in the zero-truncated bivariate Poisson regression models are also derived in this study.     

Paying More than Necessary? The Wage Cushion in Germany

In Germany, more than 40 per cent of plants covered by collective agreements pay wages above the level stipulated in the agreement, giving rise to a wage cushion between actual and contractual wages. Cross‐sectional and fixed‐effects estimations indicate that the wage cushion mainly varies with the profit situation of the plant and with indicators of labour shortage and the business cycle. Whereas plants bound by multi‐employer agreements seem to pay wage premiums in order to overcome the restrictions imposed by the rather centralized bargaining system in (western) Germany, plants that use single‐employer agreements are significantly less likely to have wage cushions.     

Product evaluation bias under minimal group situations

The present study was designed to identify the source of product evaluation bias. Three experiments were conducted to see if the evaluation bias occur in the minimal group situation defined by [Tajifel, H., Billig, M., Bundy, R., & Flament, C. (1971). Social categorization and intergroup behavior. European Journal of Social Psychology, 1, 149–178], and to determine the factors responsible for making evaluation bias. Relative measure of product evaluation played a major role in producing an evaluation bias in minimal group situation. Similarly, relative measure of distribution was a critical factor affecting distribution bias. The possible boundary condition of [Tajifel, H., Billig, M., Bundy, R., & Flament, C. (1971). Social categorization and intergroup behavior. European Journal of Social Psychology, 1, 149–178] minimal group situation in evaluation bias were also discussed.     

Subcontracting and the incidence of change orders in procurement contracts

In public procurement, most contracts are renegotiated ex post and involve subcontractors. We examine whether there is a causal link between subcontractor use and the incidence of change orders to amend the original scope of a project. Since subcontracting is likely related to unobserved project complexity, we use a novel IV, the predicted level of subcontracting from a method modeled after Christakis et al. (2010), to estimate the likelihood of renegotiation. The results establish that subcontractors are associated with an increased likelihood of change orders as well as a higher dollar amount renegotiated.