Maximum Likelihood Methods for Log-Linear Models When Expected Frequencies are Subjected to Linear Constraints

1986 ◽  
Vol 81 (394) ◽  
pp. 477 ◽  
Author(s):  
Michael Haber ◽  
Morton B. Brown
Keyword(s):  
Maximum Likelihood ◽  
Linear Models ◽  
Linear Constraints ◽  
Likelihood Methods ◽  
Maximum Likelihood Methods ◽  
Log Linear

scholarly journals Estimation of Log-Linear-Binomial Distribution with Applications

2010 ◽  
Vol 2010 ◽  
pp. 1-13
Author(s):  
Elsayed Ali Habib
Keyword(s):  
Maximum Likelihood ◽  
Binomial Distribution ◽  
Broad Class ◽  
Genetic Data ◽  
Sign Test ◽  
Likelihood Methods ◽  
Bernoulli Random Variables ◽  
Maximum Likelihood Methods ◽  
Two Parameters ◽  
Log Linear

Log-linear-binomial distribution was introduced for describing the behavior of the sum of dependent Bernoulli random variables. The distribution is a generalization of binomial distribution that allows construction of a broad class of distributions. In this paper, we consider the problem of estimating the two parameters of log-linearbinomial distribution by moment and maximum likelihood methods. The distribution is used to fit genetic data and to obtain the sampling distribution of the sign test under dependence among trials.

scholarly journals Selecting between causal and noncausal models with quantile autoregressions

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Alain Hecq ◽  
Li Sun
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Latin American ◽  
Financial Time Series ◽  
Selection Criterion ◽  
Likelihood Methods ◽  
Approximate Maximum Likelihood ◽  
Financial Time ◽  
Maximum Likelihood Methods ◽  
Latin American Countries

AbstractWe propose a model selection criterion to detect purely causal from purely noncausal models in the framework of quantile autoregressions (QAR). We also present asymptotics for the i.i.d. case with regularly varying distributed innovations in QAR. This new modelling perspective is appealing for investigating the presence of bubbles in economic and financial time series, and is an alternative to approximate maximum likelihood methods. We illustrate our analysis using hyperinflation episodes of Latin American countries.

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