Sample size determination is among the most commonly encountered tasks in statistical practice. A broad range of frequentist and Bayesian methods for sample size determination can be described as ...
This paper considers estimation of the unknown size N of a population based on multiple capture-recapture samples. We extend the Bayesian multiple recapture model to accommodate possible heterogeneity ...
Bayesian methods have emerged as a pivotal framework in the design and analysis of clinical trials, offering a systematic approach for updating evidence as new data become available. By utilising ...
Bayesian methods in Structural Equation Modeling (SEM) represent a paradigm shift in statistical analysis, integrating prior beliefs with empirical data to derive robust parameter estimates. This ...
Articulate the primary interpretations of probability theory and the role these interpretations play in Bayesian inference Use Bayesian inference to solve real-world statistics and data science ...
Download PDF More Formats on IMF eLibrary Order a Print Copy Create Citation The paper constructs a new output gap measure for Vietnam by applying Bayesian methods to a two-equation AS-AD model, while ...
We suggest a new method for integrating volatility information for estimating the value-at-risk and conditional value-at-risk of a portfolio. This new method is developed from the perspective of ...
Improved Cancer-Specific Risk Stratification by the Lymph Node Ratio-Based Nomogram: A Potential Role in Guiding Postoperative Management Decisions for Oral Cavity Carcinoma We propose a novel ...
Cobimetinib Plus Vemurafenib in Patients With Colorectal Cancer With BRAF Mutations: Results From the Targeted Agent and Profiling Utilization Registry (TAPUR) Study We divided the borrowing ...
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