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

Moment inequalities provide a framework to extract reliable information from models where parameters cannot be precisely identified. Owing to either optimisation behaviour by individual agents or ...

Well-known concepts in statistical inference and sampling theory are used to develop recommendations for planning and analyzing the results of quantitative surveys of freshwater mollusks. Two methods ...

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 ...

Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision-making. In both traditional ...

Introduces exploratory data analysis, probability theory, statistical inference, and data modeling. Topics include discrete and continuous probability distributions, expectation, laws of large numbers ...

Statistical models predict stock trends using historical data and mathematical equations. Common statistical models include regression, time series, and risk assessment tools. Effective use depends on ...

It has been estimated that about 30% of the genes in the human genome are regulated by microRNAs (miRNAs). These are short RNA sequences that can down-regulate the levels of mRNAs or proteins in ...