Maximum likelihood estimation

As explained in Statistics Slam Dunk

Maximum likelihood estimation is a parameter-estimation method that selects values maximizing the likelihood of the observed data under a model.

How Statistics Slam Dunk covers it

How deep it goes

Defines the logit equation and demonstrates glm() fitting, then defers broader model-result evaluation.

Examples

Demonstrates playoff prediction from z_o_pts and compares it with a model using z_pts.

What it assumes

Assumes a binary target and predictor variable, while explaining why ordinary linear regression cannot safely model probabilities.

What you can do after

Apply it via base R glm() to fit binary models and evaluate thresholded playoff predictions.

How it connects

Connects logistic regression with generalized linear models, binary classification, linear regression, and log odds.

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Also explained in

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  Math and Architectures of Deep LearningA parameter-estimation method that chooses values maximizing the likelihood of the observed data under a probabilistic model.
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  liveBookmanning.com
  Causal AIMaximum likelihood estimation selects parameter values that maximize the probability or likelihood of the observed data.
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  liveBookmanning.com
  Grokking StatisticsA parameter-estimation technique that selects parameter values maximizing the likelihood of the observed data.

Compare how 4 books explain maximum likelihood estimation →

Related concepts

• logistic regressionused by · Logistic regression is a statistical classification model that estimates categorical outcome probabilities by applying a logistic or softmax function to a linear combination of input features.
• likelihooduses · A likelihood is the probability of observed data under a specified probabilistic model, regarded as a function of candidate hypotheses or parameter values.
• optimizationuses · Optimization is the systematic selection or adjustment of values, actions, or system designs to improve a specified objective, often subject to constraints.
• ordinary least squaresequivalent to · Ordinary least squares is a regression parameter-estimation method that minimizes the sum of squared residuals between observed and predicted responses.
• categorical distributionequivalent to · A categorical distribution is a discrete probability distribution over a finite set of mutually exclusive categories or outcomes.
• bayesian statisticscontrasts with · Bayesian statistics is a framework for statistical inference that combines prior probability distributions with observed-data likelihoods to update probabilities over hypotheses or unknown quantities.

This explainer was generated by Manning's knowledge graph from the text of Statistics Slam Dunk. The section links point to the parts of the book it draws on.