Nature

Empirical Likelihood Methods in Statistical Inference

Empirical likelihood methods have emerged as a robust, non‐parametric framework for statistical inference that skilfully bypasses the need for strong parametric assumptions. By constructing likelihood ...
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Mastering the Basics: The Fundamentals of Statistics and Inference

Statistics is a branch of math that involves the collection, description, analysis, and inference of conclusions from quantitative data. But what is a statistic? Let’s find out. The word statistic is ...
ascopubs.org

Postprediction Inference for Clinical Characteristics Extracted With Machine Learning on Electronic Health Records

Real-world data (RWD) derived from electronic health records (EHRs) are often used to understand population-level relationships between patient characteristics and cancer outcomes. Machine learning ...
JSTOR Daily

Bayesian Inference in Hidden Markov Models through the Reversible Jump Markov Chain Monte Carlo Method

Journal of the Royal Statistical Society. Series B (Statistical Methodology), Vol. 62, No. 1 (2000), pp. 57-75 (19 pages) Hidden Markov models form an extension of mixture models which provides a ...
JSTOR Daily

Bound and collapse Bayesian reject inference for credit scoring

Reject inference is a method for inferring how a rejected credit applicant would have behaved had credit been granted. Credit-quality data on rejected applicants are usually missing not at random ...
EurekAlert!

HKU Ecologist applied a novel statistical method to analysing causal inference and revealed importance of climate change in controlling deep-sea biodiversity

Which of temperature or food is more important for the richness of deep-sea animals? Dr Moriaki YASUHARA from the School of Biological Sciences, the Research Division for Ecology & Biodiversity, and ...
CU Boulder News & Events

Causal inference under selection on observables in operations management research: Matching methods and synthetic controls

The majority of recent empirical papers in operations management (OM) employ observational data to investigate the causal effects of a treatment, such as program or policy adoption. However, as ...