Measurement error models address the deviation between observed and true values, thereby refining the reliability of statistical inference. These frameworks are ...

Consider a statistical model depending upon the parameters θ and ψ. Statistical procedures for drawing inference on θ (the structural parameter) can be based on a submodel obtained by a conditioning ...

Statistics is a branch of math that involves the collection, description, analysis, and inference of conclusions from ...

This online data science specialization is designed to provide you with a solid foundation in probability theory in preparation for the broader study of statistics. The specialization also introduces ...

Bayesian statistics represents a powerful framework for data analysis that centres on Bayes’ theorem, enabling researchers to update existing beliefs with incoming evidence. By combining prior ...

A general framework for a local influence analysis is developed for sufficient dimension reduction when the data likelihood is absent and the inference result is a space rather than a vector. A clear ...