Estimating Moments: GEV and GPD

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Statistics for Data Science: Introduction to Statistical Methods

Covers the fundamental concepts of statistics and their application in data science.

Estimators and Bias

Explores estimators, bias, and efficiency in statistics, emphasizing the trade-off between bias and variability.

Bias, Variance, Consistency, EMV

Covers bias, variance, mean squared error, consistency, and maximum likelihood estimation in the Poisson model.

Estimating Parameters: Confidence Intervals

Explores estimating parameters through confidence intervals in linear regression and statistics.

Estimation Methods

Covers various methods for estimating model parameters, such as method of moments and maximum likelihood estimation.

Estimation and Analysis of Deviance

Covers estimation of unknown parameters, analyzing model fit, bird prey response, and model diagnostics.

Extreme Values: Applications and Probability Framework

Explores extreme values in random variables, applications in environmental factors, reliability modeling, block maxima distribution, and the Generalized Extreme Value distribution.

Estimating Random Variables

Covers the estimation of random variables, including expectations, variances, and quantiles.

Normal Distribution: Properties and Calculations

Covers the normal distribution, including its properties and calculations.

Parameter Estimation

Discusses parameter estimation, including checks, quality, distribution, and statistical properties of estimates.