Bayesian Variable Selection for Gaussian copula regression models.
Bayesian Variable Selection for Gaussian copula regression models.
复制标题
高斯copula回归模型的贝叶斯变量选择。
DOI：
10.1080/10618600.2020.1840997
复制
发表时间：
2020-12-10
期刊：
影响因子：
--
通讯作者：
Bottolo L
中科院分区：
文献类型：
--
作者：
Alexopoulos A;Bottolo L
我们开发了一种新的贝叶斯方法来选择不同类型的多个响应的回归模型中的重要预测。稀疏高斯copula回归模型用于解释离散和/或连续响应的任何组合及其与一组预测因子的关联之间的多变量依赖关系。我们利用参数扩展的数据扩充策略，构造了一个马尔可夫链蒙特卡罗算法，用于估计模型的参数和潜变量。基于高斯隐变量的中心参数化，我们设计了一个固定维的建议分布来联合更新重要预测变量的潜在二进制向量和相应的非零回归系数。对于高斯响应和可以建模为高斯响应的依赖版本的结果，该提议导致Metropolis-Hastings步骤，该步骤允许有效探索预测因子的模型空间。所提出的策略进行了测试模拟数据和应用到真实的数据集，其中的响应包括低强度计数，二进制，有序和连续变量。
We develop a novel Bayesian method to select important predictors in regression models with multiple responses of diverse types. A sparse Gaussian copula regression model is used to account for the multivariate dependencies between any combination of discrete and/or continuous responses and their association with a set of predictors. We utilize the parameter expansion for data augmentation strategy to construct a Markov chain Monte Carlo algorithm for the estimation of the parameters and the latent variables of the model. Based on a centered parametrization of the Gaussian latent variables, we design a fixed-dimensional proposal distribution to update jointly the latent binary vectors of important predictors and the corresponding non-zero regression coefficients. For Gaussian responses and for outcomes that can be modeled as a dependent version of a Gaussian response, this proposal leads to a Metropolis-Hastings step that allows an efficient exploration of the predictors’ model space. The proposed strategy is tested on simulated data and applied to real data sets in which the responses consist of low-intensity counts, binary, ordinal and continuous variables.
登录
查看更多内容
影响因子：
4.4
作者：
Bornn, Luke;Caron, Francois
通讯作者：
Caron, Francois
影响因子：
16.6
作者：
Davies G;Lam M;Harris SE;Trampush JW;Luciano M;Hill WD;Hagenaars SP;Ritchie SJ;Marioni RE;Fawns-Ritchie C;Liewald DCM;Okely JA;Ahola-Olli AV;Barnes CLK;Bertram L;Bis JC;Burdick KE;Christoforou A;DeRosse P;Djurovic S;Espeseth T;Giakoumaki S;Giddaluru S;Gustavson DE;Hayward C;Hofer E;Ikram MA;Karlsson R;Knowles E;Lahti J;Leber M;Li S;Mather KA;Melle I;Morris D;Oldmeadow C;Palviainen T;Payton A;Pazoki R;Petrovic K;Reynolds CA;Sargurupremraj M;Scholz M;Smith JA;Smith AV;Terzikhan N;Thalamuthu A;Trompet S;van der Lee SJ;Ware EB;Windham BG;Wright MJ;Yang J;Yu J;Ames D;Amin N;Amouyel P;Andreassen OA;Armstrong NJ;Assareh AA;Attia JR;Attix D;Avramopoulos D;Bennett DA;Böhmer AC;Boyle PA;Brodaty H;Campbell H;Cannon TD;Cirulli ET;Congdon E;Conley ED;Corley J;Cox SR;Dale AM;Dehghan A;Dick D;Dickinson D;Eriksson JG;Evangelou E;Faul JD;Ford I;Freimer NA;Gao H;Giegling I;Gillespie NA;Gordon SD;Gottesman RF;Griswold ME;Gudnason V;Harris TB;Hartmann AM;Hatzimanolis A;Heiss G;Holliday EG;Joshi PK;Kähönen M;Kardia SLR;Karlsson I;Kleineidam L;Knopman DS;Kochan NA;Konte B;Kwok JB;Le Hellard S;Lee T;Lehtimäki T;Li SC;Lill CM;Liu T;Koini M;London E;Longstreth WT Jr;Lopez OL;Loukola A;Luck T;Lundervold AJ;Lundquist A;Lyytikäinen LP;Martin NG;Montgomery GW;Murray AD;Need AC;Noordam R;Nyberg L;Ollier W;Papenberg G;Pattie A;Polasek O;Poldrack RA;Psaty BM;Reppermund S;Riedel-Heller SG;Rose RJ;Rotter JI;Roussos P;Rovio SP;Saba Y;Sabb FW;Sachdev PS;Satizabal CL;Schmid M;Scott RJ;Scult MA;Simino J;Slagboom PE;Smyrnis N;Soumaré A;Stefanis NC;Stott DJ;Straub RE;Sundet K;Taylor AM;Taylor KD;Tzoulaki I;Tzourio C;Uitterlinden A;Vitart V;Voineskos AN;Kaprio J;Wagner M;Wagner H;Weinhold L;Wen KH;Widen E;Yang Q;Zhao W;Adams HHH;Arking DE;Bilder RM;Bitsios P;Boerwinkle E;Chiba-Falek O;Corvin A;De Jager PL;Debette S;Donohoe G;Elliott P;Fitzpatrick AL;Gill M;Glahn DC;Hägg S;Hansell NK;Hariri AR;Ikram MK;Jukema JW;Vuoksimaa E;Keller MC;Kremen WS;Launer L;Lindenberger U;Palotie A;Pedersen NL;Pendleton N;Porteous DJ;Räikkönen K;Raitakari OT;Ramirez A;Reinvang I;Rudan I;Dan Rujescu;Schmidt R;Schmidt H;Schofield PW;Schofield PR;Starr JM;Steen VM;Trollor JN;Turner ST;Van Duijn CM;Villringer A;Weinberger DR;Weir DR;Wilson JF;Malhotra A;McIntosh AM;Gale CR;Seshadri S;Mosley TH Jr;Bressler J;Lencz T;Deary IJ
通讯作者：
Deary IJ
影响因子：
2.4
作者：
Holmes, CC;Denison, DGT;Mallick, BK
通讯作者：
Mallick, BK
影响因子：
2.2
作者：
Dellaportas, P;Forster, JJ;Ntzoufras, I
通讯作者：
Ntzoufras, I
影响因子：
4.4
作者：
Bottolo, Leonard;Richardson, Sylvia
通讯作者：
Richardson, Sylvia