Identify characteristics of “good” estimators and be able to compare competing estimators. Construct sound estimators using the techniques of maximum likelihood and method of moments estimation.

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 ...

Statistical significance is not the same as practical (or clinical) significance. All point estimates (statistics calculated from the sample data) are subject to sampling variation, and all methods of ...

Explain what is meant by statistical inference. Define a point estimate and population parameter and list common types of point estimates and parameters Identify point estimates and parameters when ...

The course covers the probability, distribution theory and statistical inference needed for third year courses in statistics and econometrics ... Lent term: Sampling distributions. Criteria of ...

Methods of order restricted inference are used in computing maximum likelihood estimators and developing distribution theory for inverse problems ... while the later chapters are written with graduate ...

Prior to joining Drexel in Fall 2023, he held postdoctoral positions at Washington University in St. Louis and the University of Utah. His current work focuses on statistical inference (estimation, ...

Her research interests include statistical inference on high-dimensional directional data, geometrically anisotropic spatial process and spatial model estimation with matrix-free computation methods ...

Statistical decision theory: risk, decision rules, loss and utility functions, Bayesian expected loss, Frequentist risk. Bayesian Inference: Bayes theorem, prior, posterior and predictive ...