The Equivalence of Maximum Likelihood and Weighted Least Squares Estimate in the Exponential Family

1973 ◽  
Vol 68 (341) ◽  
pp. 199 ◽  
Author(s):  
Edwin L. Bradley
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Exponential Family ◽  
Weighted Least Squares ◽  
Least Squares Estimate ◽  
Weighted Least Squares Estimate

WAVELET ESTIMATORS FOR LONG MEMORY IN STOCK MARKETS

2009 ◽  
Vol 12 (03) ◽  
pp. 297-317 ◽  
Author(s):  
ANOUAR BEN MABROUK ◽  
HEDI KORTAS ◽  
SAMIR BEN AMMOU
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Stock Returns ◽  
Long Memory ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Estimation Accuracy ◽  
Approximate Maximum Likelihood ◽  
Approximate Maximum Likelihood Estimator ◽  
Wavelet Estimators

In this paper, fractional integrating dynamics in the return and the volatility series of stock market indices are investigated. The investigation is conducted using wavelet ordinary least squares, wavelet weighted least squares and the approximate Maximum Likelihood estimator. It is shown that the long memory property in stock returns is approximately associated with emerging markets rather than developed ones while strong evidence of long range dependence is found for all volatility series. The relevance of the wavelet-based estimators, especially, the approximate Maximum Likelihood and the weighted least squares techniques is proved in terms of stability and estimation accuracy.

Jaya algorithm in estimation of P[X > Y] for two parameter Weibull distribution

2022 ◽  
Vol 7 (2) ◽  
pp. 2820-2839
Author(s):  
Saurabh L. Raikar ◽  
◽  
Dr. Rajesh S. Prabhu Gaonkar ◽  
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Weibull Distribution ◽  
Least Squares ◽  
Weighted Least Squares ◽  
Real Life ◽  
Likelihood Estimation ◽  
Estimation Methods ◽  
Jaya Algorithm ◽  
Two Parameter

<abstract> <p>Jaya algorithm is a highly effective recent metaheuristic technique. This article presents a simple, precise, and faster method to estimate stress strength reliability for a two-parameter, Weibull distribution with common scale parameters but different shape parameters. The three most widely used estimation methods, namely the maximum likelihood estimation, least squares, and weighted least squares have been used, and their comparative analysis in estimating reliability has been presented. The simulation studies are carried out with different parameters and sample sizes to validate the proposed methodology. The technique is also applied to real-life data to demonstrate its implementation. The results show that the proposed methodology's reliability estimates are close to the actual values and proceeds closer as the sample size increases for all estimation methods. Jaya algorithm with maximum likelihood estimation outperforms the other methods regarding the bias and mean squared error.</p> </abstract>

A Comparison of Two Logit Models in the Analysis of Qualitative Marketing Data

1979 ◽  
Vol 16 (4) ◽  
pp. 533-538 ◽  
Author(s):  
David Flath ◽  
E. W. Leonard
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Logit Model ◽  
Weighted Least Squares ◽  
Interaction Effects ◽  
Logit Models ◽  
Marketing Data

The authors compare the application of two logit models for the analysis of qualitative marketing data. A weighted least squares logit model is compared with a maximum likelihood logit model different from that mentioned by Green et ai. Empirical applications are used to compare the models. Suggestions are presented for interpreting and reporting the results of logit-type models, with special attention to interaction effects.

TOTAL ODDS AND OTHER OBJECTIVES FOR CLUSTERING VIA MULTINOMIAL-LOGIT MODEL

2012 ◽  
Vol 04 (03) ◽  
pp. 1250019 ◽  
Author(s):  
STAN LIPOVETSKY
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Logit Model ◽  
Weighted Least Squares ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Total Probability ◽  
Numerical Examples ◽  
Newton Raphson

This work considers maximum likelihood objectives for estimating the probability of each multivariate observation's assignment to one particular cluster or to one or more clusters. Combining both objectives yields a maximization of the total probability odds of belonging to one or another cluster. The gradient of the total odds objective can be reduced to the multinomial-logit probabilities leading to a convenient Newton–Raphson clustering procedure presented via an iteratively re-weighted least squares technique. Besides the total odds, several other new objectives are also considered, and numerical examples are discussed.

Sign in / Sign up

Export Citation Format

Share Document