Sparse Bayesian multinomial probit regression model with correlation prior for high-dimensional data classification

2016 ◽  
Vol 119 ◽  
pp. 241-247 ◽  
Author(s):  
Aijun Yang ◽  
Xuejun Jiang ◽  
Pengfei Liu ◽  
Jinguan Lin
Keyword(s):  
Regression Model ◽  
High Dimensional Data ◽  
Data Classification ◽  
High Dimensional ◽  
Multinomial Probit ◽  
Probit Regression ◽  
Correlation Prior ◽  
Multinomial Probit Regression

A modular eigen subspace scheme for high-dimensional data classification

2004 ◽  
Vol 20 (7) ◽  
pp. 1131-1143 ◽  
Author(s):  
Yang-Lang Chang ◽  
Chin-Chuan Han ◽  
Fan-Di Jou ◽  
Kuo-Chin Fan ◽  
K.S. Chen ◽  
...  
Keyword(s):  
High Dimensional Data ◽  
Data Classification ◽  
High Dimensional

Cooking fuel choices of households in urban areas in Uganda: a multinomial probit regression analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Joseph Elasu ◽  
Bright Richard Richard ◽  
Muyiwa S. Adaramola
Keyword(s):  
Urban Areas ◽  
Sociodemographic Factors ◽  
Multinomial Probit ◽  
Probit Regression ◽  
Cooking Fuel ◽  
Content Type ◽  
Explanatory Variables ◽  
Section Data ◽  
Making Choices ◽  
Multinomial Probit Regression

PurposeThis study explores the economic and sociodemographic factors that influence households' decisions on the type of fuel used for cooking in urban areas in Uganda.Design/methodology/approachIn total, two cross-section data surveyed by the Uganda Bureau of Statistics (UBOS) in 2012/13 and 2016/17 were used to analyze consumption of energy for cooking purposes in urban areas of Uganda. This paper employed a multinomial probit regression model and the corresponding marginal effects to analyze cooking fuel choices, which are biomass, electricity and gas and kerosene combined.FindingsThe results showed that household expenditure was statistically significant for the choice of cooking fuel chosen. Furthermore, kitchen type, dwelling type and apartment tenure type are found to be significantly influence the choice of household cooking fuel decisions.Originality/valueThis study takes into consideration the combined influence of the kitchen type, dwelling and tenure type as explanatory variables for the choice of cooking fuel for households in urban areas in Uganda. These factors have not been considered in previous studies done in Uganda, especially within the context of urban households when making choices for cooking fuel.

An efficient random forests algorithm for high dimensional data classification

2018 ◽  
Vol 12 (4) ◽  
pp. 953-972 ◽  
Author(s):  
Qiang Wang ◽  
Thanh-Tung Nguyen ◽  
Joshua Z. Huang ◽  
Thuy Thi Nguyen
Keyword(s):  
Random Forests ◽  
High Dimensional Data ◽  
Data Classification ◽  
High Dimensional
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