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Introduction to Statistical Modelling and Inference

Introduction to Statistical Modelling and Inference

by Murray Aitkin
Hardback
Publication Date: 30/09/2022
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Features






Probability models are developed from the shape of the sample empirical cumulative distribution function, (cdf) or a transformation of it.



Bounds for the value of the population cumulative distribution function are obtained from the Beta distribution at each point of the empirical cdf.



Bayes's theorem is developed from the properties of the screening test for a rare condition.



The multinomial distribution provides an always-true model for any randomly sampled data.



The model-free bootstrap method for finding the precision of a sample estimate has a model-based parallel - the Bayesian bootstrap - based on the always-true multinomial distribution.



The Bayesian posterior distributions of model parameters can be obtained from the maximum likelihood analysis of the model.
ISBN:
9781032105710
9781032105710
Category:
Probability & statistics
Format:
Hardback
Publication Date:
30-09-2022
Publisher:
Taylor & Francis Ltd
Country of origin:
United Kingdom
Pages:
374
Dimensions (mm):
254x178mm
Weight:
0.96kg

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You can find this item in:

Mathematics Bayesian inference Probability & statistics Economic statistics
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