Fitting multilevel multivariate models with missing data in responses and covariates that may include interactions and non-linear terms

The paper extends existing models for multilevel multivariate data with mixed response types to handle quite general types and patterns of missing data values in a wide range of multilevel generalized linear models. It proposes an efficient Bayesian modelling approach that allows missing values in covariates, including models where there are interactions or other functions of covariates such as polynomials. The procedure can also be used to produce multiply imputed complete data sets. A simulation study is presented as well as the analysis of a longitudinal data set. The paper also shows how existing multiprocess models for handling endogeneity can be extended by the framework proposed

Microdata Imputations and Macrodata Implications: Evidence from the Ifo Business Survey

A widespread method for now- and forecasting economic macro level parameters such as GDP growth rates are survey-based indicators which contain early information in contrast to official data. But surveys are commonly affected by nonresponding units which can produce biases if these missing values can not be regarded as missing at random. As many papers examined the effect of nonresponse in individual or household surveys, only less is known in the case of business surveys. So, literature leaves a gap on this issue. For this reason, we analyse and impute the missing observations in the Ifo Business Survey, a large business survey in Germany. The most prominent result of this survey is the Ifo Business Climate Index, a leading indicator for the German business cycle. To reflect the underlying latent data generating process, we compare different imputation approaches for longitudinal data. After this, the microdata are aggregated and the results are compared with the original indicators to evaluate their implications on the macro level. Finally, we show that the bias is minimal and ignorable

Bayesian model search and multilevel inference for SNP association studies

Technological advances in genotyping have given rise to hypothesis-based association studies of increasing scope. As a result, the scientific hypotheses addressed by these studies have become more complex and more difficult to address using existing analytic methodologies. Obstacles to analysis include inference in the face of multiple comparisons, complications arising from correlations among the SNPs (single nucleotide polymorphisms), choice of their genetic parametrization and missing data. In this paper we present an efficient Bayesian model search strategy that searches over the space of genetic markers and their genetic parametrization. The resulting method for Multilevel Inference of SNP Associations, MISA, allows computation of multilevel posterior probabilities and Bayes factors at the global, gene and SNP level, with the prior distribution on SNP inclusion in the model providing an intrinsic multiplicity correction. We use simulated data sets to characterize MISA's statistical power, and show that MISA has higher power to detect association than standard procedures. Using data from the North Carolina Ovarian Cancer Study (NCOCS), MISA identifies variants that were not identified by standard methods and have been externally ``validated'' in independent studies. We examine sensitivity of the NCOCS results to prior choice and method for imputing missing data. MISA is available in an R package on CRAN.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS322 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Effects of Information and Country of Origin On Chinese Consumer Preferences for Wine: An Experimental Approach in the Field

Wine is a product whose value largely depends on the reputation associated with its region of production. China is a newcomer and latecomer to wine production and consumption. Wine consumption, especially imported wine, rarely exists outside of major urban areas. Therefore, understanding the Chinese local markets and consumer preference for wine products is important for foreign wine producers. WTP (Willingness-to-Pay), in economics, is the maximum amount a person would be willing to pay for a good, which is a useful tool to address consumers’ preference. In our study, we investigate the effects of information and origin of production on Chinese consumers’ WTP for wine. By using a second-price sealed-bid auction mechanism, which was first developed by Vickrey (1961), we organized experimental auctions in both Beijing and Shanghai, China. The items for auctions are four different wine products originated in China, France, USA, and Australia. And there are two comparison groups, with or without information exposure. With 436 participants in total, our experiments collected data on their WTP’s and socio-demographics. Our data shows that participants would like to pay the highest price for the wine from France, while their WTP for the Chinese wine is the lowest among the four. Furthermore, we find important factors affecting their WTP for wine, including age, gender, employment status, education status, household income, and household size. Our results provide meaningful and insightful marketing suggestions for the “new world” and Chinese wine producers, such as the target consumers and pricing strategy.wine consumption, willingness-to-pay, second price auction, Consumer/Household Economics, Food Consumption/Nutrition/Food Safety, Marketing,