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Everything about Maximum likelihood estimation
Method of estimating the parameters of a statistical model, given observations
In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed data. This is achieved by maximizing a likelihood function so that, under the assumed statistical model, the observed data is most probable. The point in the parameter space that maximizes the likelihood function is called the maximum likelihood estimate. The logic of maximum likelihood is both intuitive and flexible, and as such the method has become a dominant means of statistical inference. Wikipedia →
🧠Start here: a 6-step reading path
- Read the overview: Maximum likelihood estimationA 5-minute orientation
- Skim a recent paper: Maximum likelihood estimation for score-driven modelsInfluential work since 2022
- Pick up a classic book: The pulse-pair algorithm as a robust estimator of turbulent weather spectral parameters using airborne pulse Doppler radarThe most-reprinted book on the subject
- Read the landmark paper: IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood PhylogeniesCited 25,776 times
- Go sideways: Maximum spacing estimationA closely related topic
- Go sideways: Point estimationA closely related topic
đź“– Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies
New Algorithms and Methods to Estimate Maximum-Likelihood Phylogenies: Assessing the Performance of PhyML 3.0
A Simple, Fast, and Accurate Algorithm to Estimate Large Phylogenies by Maximum Likelihood
MAXIMUM LIKELIHOOD ESTIMATION AND INFERENCE ON COINTEGRATION — WITH APPLICATIONS TO THE DEMAND FOR MONEY
Fast Stable Restricted Maximum Likelihood and Marginal Likelihood Estimation of Semiparametric Generalized Linear Models
Bias reduction of maximum likelihood estimates
🆕 Influential since 2022
Maximum likelihood estimation for score-driven models
Maximum Likelihood Estimation in Data-Driven Modeling and Control
PINT: Maximum-likelihood Estimation of Pulsar Timing Noise Parameters
CherryML: scalable maximum likelihood estimation of phylogenetic models
Maximum Likelihood Estimation in the Additive Hazards Model
đź“š Books worth reading
- 📕The pulse-pair algorithm as a robust estimator of turbulent weather spectral parameters using airborne pulse Doppler radarErnest G. Baxa · 1991
- 📕Extraction of lateral-directional stability and control derivatives for the basic F-18 aircraft at high angles of attackKenneth W. Iliff · 1997
- 📕Flight-determined subsonic longitudinal stability and control derivatives of the F-18 High Angle of Attack Research Vehicle (HARV) with thrust vectoringKenneth W. Iliff · 1997
- 📕Determining the accuracy of aerodynamic model parameters estimated from flight test dataEugene A. Morelli · 1995
- 📕A hybrid M-algorithm/sequential decoder for convolutional and Trellis codesFu-Quan Wang · 1990
- 📕Flight-determined, subsonic, lateral-directional stability and control derivatives of the thrust-vectoring F-18 high angle of attack research vehicle (HARV), and comparisons to the basic F-18 and predicted derivatesKenneth W. Lliff · 1999
- 📕The effect of lossy image compression on image classificationJustin D. Paola · 1995
- 📕Validation of the ERBE scanner scene identification methodologySastri Vermury · 1985
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
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