Bayesian estimation of clustered dependence structures in functional neuroconnectivity

Motivated by the need to model joint dependence between regions of interest in functional neuroconnectivity for efficient inference, we propose a new sampling-based Bayesian clustering approach for covariance structures of high-dimensional Gaussian outcomes. The key technique is based on a Dirichlet process that clusters covariance sub-matrices into independent groups of outcomes, thereby naturally inducing sparsity in the whole brain connectivity matrix. A new split-merge algorithm is employed to improve the mixing of the Markov chain sampling that is shown empirically to recover both uniform and Dirichlet partitions with high accuracy. We investigate the empirical performance of the proposed method through extensive simulations. Finally, the proposed approach is used to group regions of interest into functionally independent groups in the Autism Brain Imaging Data Exchange participants with autism spectrum disorder and attention-deficit/hyperactivity disorder.Comment: 31 pages, 7 figures, 2 table

Ridge Fuzzy Regression Modelling for Solving Multicollinearity

This paper proposes an α-level estimation algorithm for ridge fuzzy regression modeling, addressing the multicollinearity phenomenon in the fuzzy linear regression setting. By incorporating α-levels in the estimation procedure, we are able to construct a fuzzy ridge estimator which does not depend on the distance between fuzzy numbers. An optimized α-level estimation algorithm is selected which minimizes the root mean squares for fuzzy data. Simulation experiments and an empirical study comparing the proposed ridge fuzzy regression with fuzzy linear regression is presented. Results show that the proposed model can control the effect of multicollinearity from moderate to extreme levels of correlation between covariates, across a wide spectrum of spreads for the fuzzy response

The relationship between price and quality in durable product categories with private label brands

Purpose This paper aims to investigate price and objective-quality in durable product categories containing national and private-label (PL) brands. Design/methodology/approach Using data from consumer reports objective-test results of 14,476 durable products available in the US the authors identified product categories containing both national and PL brands; constructed relative price- and quality-indices for each category; calculated price and quality differentials for each category then modeled the relationship between them; estimated the price premium associated with national brands (NBs); and computed price–quality (PQ) correlations for each category. The authors also analyzed the same relationships using subjective brand-perception data collected from 240 consumers. Findings Overall the price of NBs in durable products was substantially higher than the price of PL brands despite there being little to no difference in quality levels overall, with the proportion of categories having higher PL quality nearly equaling that of categories having superior NB quality. Correlation between price and quality was moderate. Accuracy of consumer perceptions varied depending on the importance of brand in the purchase decisions for particular product categories. Originality/value This paper uses a large objective dataset spanning a period of more than eight years to assess price and quality for durable goods in categories offering PL brands. It addresses an under-studied area, that of PL brands for higher-priced, longer-lasting products. The findings contribute to an existing understanding of PLs, especially in the domain of durable-goods, as well as to the body of research in the area of PQ relationships. It also adds to our understanding of consumers’ perceptions of brand as a factor in durable product decisions and how the market aligns with those perceptions

Household Work: What's it Worth and Why?

This information can help families decide whether to work outside the home; whether to purchase life insurance and, if so, how much; and calculate the indirect costs of having children

Store brand vs. national brand prices: Willingness to pay ≠ willingness to accept

Determining the appropriate price for store brands relative to national brands is important. When setting the price, consumers’ perceptions of price and quality need to be considered. Two past approaches employed by store brand researchers to reveal consumers’ value of store brands include asking either: (1) the price discount they would need to be offered to switch from a national brand to a store brand (a measure of “willingness-to-accept”); or (2) the price premium they would be willing to pay to switch from a store brand to a national brand (a measure of “willingness-to-pay”). Research in other domains reveals that willingness-to-accept (WTA) and willingness-to-pay (WTP) estimates can diverge. We formally tested whether WTA estimates differ from WTP estimates elicited from consumers with respect to store and national brand prices. As predicted, WTA price estimates exceeded those of WTP. This pattern held regardless of whether product-quality equivalence of store and national brands was explicitly provided to respondents or whether respondents were free to make their own assumptions of product quality. Implications for private label researchers and product brand managers are discussed