Algebraic likelihood maximization avoiding the log-likelihood function and differentiation

The fact that the graph of the exponential function exp is always at or above the straight line through the origin with slope exp⁡(1) is well-known and can be easily proved using differential calculus. We provide a simple algebraic proof of that fact and use that fact to construct a template for maximizing the likelihood function that avoids the log-likelihood function and differentiation in a number of examples, including the two-parameter normal distribution family.

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Publication Details

Journal
Research in Statistics
Published
2024-08-19
DOI
https://doi.org/10.1080/27684520.2024.2376135
Citations
1
Primary Topic
Statistical Methods and Inference
Type
article
Field-Weighted Citation Impact
0.71
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article

Algebraic likelihood maximization avoiding the log-likelihood function and differentiation

S. R. Majumdar
1 citations
Research in Statistics
Statistical Methods and Inference
0.71
article

Algebraic likelihood maximization avoiding the log-likelihood function and differentiation

S. R. Majumdar
article en
1 citations

Abstract

The fact that the graph of the exponential function exp is always at or above the straight line through the origin with slope exp⁡(1) is well-known and can be easily proved using differential calculus. We provide a simple algebraic proof of that fact and use that fact to construct a template for maximizing the likelihood function that avoids the log-likelihood function and differentiation in a number of examples, including the two-parameter normal distribution family.

Research in StatisticsVol. 2(1)
University of Connecticut (US)
Reduced inequalities
Openalex Percentile: Top 27%
Statistical Methods and Inference
0.71
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