The Experts below are selected from a list of 5910 Experts worldwide ranked by ideXlab platform
Zhao Zhi - One of the best experts on this subject based on the ideXlab platform.
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BayesSUR: Bayesian Seemingly Unrelated Regression
The Comprehensive R Archive Network, 2021Co-Authors: Banterle Marco, Zhao Zhi, Bottolo Leonardo, Richardson Sylvia, Leoncio Waldir, Lewin Alexandra, Zucknick ManuelaAbstract:Bayesian Seemingly Unrelated Regression with general variable selection and dense/sparse covariance matrix. The sparse Seemingly Unrelated Regression is described in Bottolo et al. (2020) , and the software paper is in Zhao et al. (2021)
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BayesSUR: An R Package for High-Dimensional Multivariate Bayesian Variable and Covariance Selection in Linear Regression
'Foundation for Open Access Statistic', 2021Co-Authors: Zhao Zhi, Banterle Marco, Bottolo Leonardo, Richardson Sylvia, Lewin Alex, Zucknick ManuelaAbstract:In molecular biology, advances in high-throughput technologies have made it possible to study complex multivariate phenotypes and their simultaneous associations with high-dimensional genomic and other omics data, a problem that can be studied with high-dimensional multi-response Regression, where the response variables are potentially highly correlated. To this purpose, we recently introduced several multivariate Bayesian variable and covariance selection models, e.g., Bayesian estimation methods for sparse Seemingly Unrelated Regression for variable and covariance selection. Several variable selection priors have been implemented in this context, in particular the hotspot detection prior for latent variable inclusion indicators, which results in sparse variable selection for associations between predictors and multiple phenotypes. We also propose an alternative, which uses a Markov random field (MRF) prior for incorporating prior knowledge about the dependence structure of the inclusion indicators. Inference of Bayesian Seemingly Unrelated Regression (SUR) by Markov chain Monte Carlo methods is made computationally feasible by factorisation of the covariance matrix amongst the response variables. In this paper we present BayesSUR, an R package, which allows the user to easily specify and run a range of different Bayesian SUR models, which have been implemented in C++ for computational efficiency. The R package allows the specification of the models in a modular way, where the user chooses the priors for variable selection and for covariance selection separately. We demonstrate the performance of sparse SUR models with the hotspot prior and spike-and-slab MRF prior on synthetic and real data sets representing eQTL or mQTL studies and in vitro anti-cancer drug screening studies as examples for typical applications
Andrew R Willan - One of the best experts on this subject based on the ideXlab platform.
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Regression methods for cost effectiveness analysis with censored data
Statistics in Medicine, 2005Co-Authors: Andrew R Willan, Andrea MancaAbstract:A system of Seemingly Unrelated Regression equations is proposed for prognostic factor adjustment and subgroup analysis when comparing two groups in a cost-effectiveness analysis with censored data. Because of the induced dependent censoring on costs and quality-adjusted survival, inverse probability weighting is employed for parameter estimation. The method is illustrated with data from two recent examples using both survival time and quality-adjusted survival time as the measures of effectiveness.
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Regression methods for covariate adjustment and subgroup analysis for non censored cost effectiveness data
Health Economics, 2004Co-Authors: Andrew R Willan, Andrew Briggs, Jeffrey S HochAbstract:The current interest in undertaking cost-effectiveness analyses alongside clinical trials has lead to the increasing availability of patient-level data on both the costs and effectiveness of intervention. In a recent paper, we show how cost-effectiveness analysis can be undertaken in a Regression framework. In the current paper we develop a direct Regression approach to cost-effectiveness analysis by proposing the use of a system of Seemingly Unrelated Regression equations to provide a more general method for prognostic factor adjustment with emphasis on sub-group analysis. This more general method can be used in either an incremental cost-effectiveness or an incremental net-benefit approach, and does not require that the set of independent variables for costs and effectiveness be the same. Furthermore, the method can exhibit efficiency gains over Unrelated ordinary least squares Regression.
Kara M Kockelman - One of the best experts on this subject based on the ideXlab platform.
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predicting the distribution of households and employment a Seemingly Unrelated Regression model with two spatial processes
Journal of Transport Geography, 2009Co-Authors: Bin Zhou, Kara M KockelmanAbstract:Abstract Household and employment counts (by type) are key inputs to models of travel demand and air quality. For a variety of reasons, spatial dependence is very likely present in and across these counts. In order to identify the nature of these unobserved relationships, this study provides the first application of a feasible generalized spatial 3SLS estimation procedure for a Seemingly Unrelated Regression (SUR) model with two spatial processes. Statistical tests reveal that this more generalized model is superior to its constrained versions (e.g., SUR models without spatial components or with just a spatial lag or spatial error process). In the resulting model of Austin, Texas data, local land-use conditions offer substantial predictive power of household and job densities, and transportation access plays a role, as anticipated. The work demonstrates that SUR estimation of land-use intensities from parcel-level data with two types of spatial dependence is feasible and meaningful. Coupled with an upstream model of land-use type, this work offers key inputs for travel demand analyses, with transportation system performance feedback.
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predicting the distribution of households and employment a Seemingly Unrelated Regression model with two spatial processes
11th World Conference on Transport ResearchWorld Conference on Transport Research Society, 2007Co-Authors: Bin Zhou, Kara M KockelmanAbstract:Household and employment counts (by type) are key inputs to models of travel demand. For a variety of reasons, spatial dependence is very likely present in and across these counts. In order to identify the nature of these unobserved relationships, this study performs a series of Lagrange multiplier tests to confirm the co-existence of spatial lag and error processes within individual equations (6 household types and 3 employment categories). It then provides the first application of a feasible generalized spatial 3SLS estimation procedure for a Seemingly Unrelated Regression (SUR) model of these equations. In the resulting model of Austin, Texas data, local land use conditions offer substantial predictive power of households and jobs, and transportation access plays a role, as anticipated. The work demonstrates that SUR estimation of land use intensities from parcel-level data with two types of spatial dependence is feasible and meaningful. Coupled with an upstream model of land use type, this work offers the key inputs for travel demand analyses, with transportation system performance feedback.
Anita Pansari - One of the best experts on this subject based on the ideXlab platform.
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national culture economy and customer lifetime value assessing the relative impact of the drivers of customer lifetime value for a global retailer
Journal of International Marketing, 2016Co-Authors: V Kumar, Anita PansariAbstract:AbstractCustomer lifetime value (CLV), a metric used in many industries, is based on the cumulated cash flow a customer accrues during his or her lifetime. Firms have used CLV as a basis for formulating and implementing customer-specific strategies; however, these can vary across countries because of each country’s cultural and economic influences. Typically, CLV is computed with three components: purchase frequency, contribution margin, and marketing costs. In this study, the authors demonstrate that national cultural dimensions affect the drivers of purchase frequency and contribution margin and that economic factors influence the components of CLV directly. They use customer-level transaction data from a global retailer for a random sample of customers in 30 representative countries over a six-year period. They estimate the model using a Seemingly Unrelated Regression approach while accounting for the heterogeneity of customers across countries and the endogeneity of marketing costs incurred by the fir...
Hiroshi Kurata - One of the best experts on this subject based on the ideXlab platform.
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Optimal estimator under risk matrix in a Seemingly Unrelated Regression model and its generalized least squares expression
Statistical Papers, 2021Co-Authors: Shun Matsuura, Hiroshi KurataAbstract:A set of multiple Regression models whose error terms have possibly contemporaneous correlations is called a Seemingly Unrelated Regression model. In this paper, a best equivariant estimator of the Regression vector under risk matrix is established in a Seemingly Unrelated Regression model. It should be noted that an estimator optimal with respect to risk matrix remains optimal under a broad range of quadratic loss functions. A generalized least squares expression of our estimator is also presented.
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best equivariant estimator of Regression coefficients in a Seemingly Unrelated Regression model with known correlation matrix
Annals of the Institute of Statistical Mathematics, 2016Co-Authors: Hiroshi Kurata, Shun MatsuuraAbstract:Abstract This paper derives the best equivariant estimator (BEE) of the Regression coefficients of a Seemingly Unrelated Regression model with an elliptically symmetric error. Equivariance with respect to the group of location and scale transformations is considered. We assume that the correlation matrix of the error term is known. Since the correlation matrix is a maximal invariant parameter under the group action, the model treated in this paper is generated as exactly one orbit on the parameter space. It is also shown that the BEE can be viewed as a generalized least squares estimator.
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least upper bound for the covariance matrix of a generalized least squares estimator in Regression with applications to a Seemingly Unrelated Regression model and a heteroscedastic model
Annals of Statistics, 1996Co-Authors: Hiroshi Kurata, Takeaki KariyaAbstract:In a general normal Regression model, this paper first derives the least upper bound (LUB) for the covariance matrix of a generalized least squares estimator (GLSE) relative to the covariance matrix of the Gauss-Markov estimator. Second the result is applied to the (unrestricted) Zellner estimator in an N-equation Seemingly Unrelated Regression (SUR) model and to the GLSE in a heteroscedastic model.
