Multiple Random Variables—Linear Regression Maximum Likelihood Estimation

2016 ◽  
pp. 445-465
Author(s):  
Frédéric Cohen Tenoudji
Keyword(s):  
Maximum Likelihood ◽  
Linear Regression ◽  
Maximum Likelihood Estimation ◽  
Random Variables ◽  
Likelihood Estimation

scholarly journals DECONVOLUTING PREFERENCES AND ERRORS: A MODEL FOR BINOMIAL PANEL DATA

2010 ◽  
Vol 26 (6) ◽  
pp. 1846-1854 ◽  
Author(s):  
Mogens Fosgerau ◽  
Søren Feodor Nielsen
Keyword(s):  
Maximum Likelihood ◽  
Panel Data ◽  
Maximum Likelihood Estimation ◽  
Unknown Parameter ◽  
Random Variables ◽  
Likelihood Estimation ◽  
Estimation Procedure ◽  
Choice Experiments ◽  
Stated Choice ◽  
Stated Choice Experiments

In many stated choice experiments researchers observe the random variablesVt,Xt, andYt= 1{U+δ⊤Xt+ εt<Vt},t≤T, whereδis an unknown parameter andUand εtare unobservable random variables. We show that under weak assumptions the distributions ofUand εtand also the unknown parameterδcan be consistently estimated using a sieved maximum likelihood estimation procedure.

Maximum likelihood estimation in transformed linear regression with nonnormal errors

2019 ◽  
Vol 47 (4) ◽  
pp. 1864-1892
Author(s):  
Xingwei Tong ◽  
Fuqing Gao ◽  
Kani Chen ◽  
Dingjiao Cai ◽  
Jianguo Sun
Keyword(s):  
Maximum Likelihood ◽  
Linear Regression ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation

Maximum likelihood estimation of correction for dilution bias in simple linear regression using replicates from subjects with extreme first measurements

2008 ◽  
Vol 27 (22) ◽  
pp. 4397-4407 ◽  
Author(s):  
Lars Berglund ◽  
Hans Garmo ◽  
Johan Lindbäck ◽  
Kurt Svärdsudd ◽  
Björn Zethelius
Keyword(s):  
Maximum Likelihood ◽  
Linear Regression ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Simple Linear Regression
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