Estimation of the Residuals and the Coefficients Test of the Full Information Maximum Likelihood of System Equations of 4 Spot Exchange Rates in Terms of CAD/USD, DKK/USD, CHF/USD and JPY/USD.

2019 ◽  
Author(s):  
Michel Guirguis
Keyword(s):  
Maximum Likelihood ◽  
Exchange Rates ◽  
Full Information ◽  
Full Information Maximum Likelihood ◽  
System Equations

scholarly journals Maximum Likelihood and Two-Step Estimation of an Ordered-Probit Selection Model

2007 ◽  
Vol 7 (2) ◽  
pp. 167-182 ◽  
Author(s):  
Richard Chiburis ◽  
Michael Lokshin
Keyword(s):  
Monte Carlo ◽  
Maximum Likelihood ◽  
Monte Carlo Simulations ◽  
Regression Model ◽  
Selection Rule ◽  
Ordered Probit ◽  
Maximum Likelihood Estimates ◽  
Full Information ◽  
Full Information Maximum Likelihood ◽  
Two Step Estimation

We discuss the estimation of a regression model with an ordered-probit selection rule. We have written a Stata command, oheckman, that computes two-step and full-information maximum-likelihood estimates of this model. Using Monte Carlo simulations, we compare the performances of these estimators under various conditions.

scholarly journals Best (but oft-forgotten) practices: missing data methods in randomized controlled nutrition trials

2019 ◽  
Vol 109 (3) ◽  
pp. 504-508 ◽  
Author(s):  
Peng Li ◽  
Elizabeth A Stuart
Keyword(s):  
Missing Data ◽  
Maximum Likelihood ◽  
Causal Inference ◽  
Randomized Controlled Trials ◽  
Multiple Imputation ◽  
Controlled Trials ◽  
Full Information ◽  
Complete Case ◽  
Full Information Maximum Likelihood ◽  
Randomized Controlled

ABSTRACT Missing data ubiquitously occur in randomized controlled trials and may compromise the causal inference if inappropriately handled. Some problematic missing data methods such as complete case (CC) analysis and last-observation-carried-forward (LOCF) are unfortunately still common in nutrition trials. This situation is partially caused by investigator confusion on missing data assumptions for different methods. In this statistical guidance, we provide a brief introduction of missing data mechanisms and the unreasonable assumptions that underlie CC and LOCF and recommend 2 appropriate missing data methods: multiple imputation and full information maximum likelihood.

Sign in / Sign up

Export Citation Format

Share Document