Introduction to statistical modelling and inference (Record no. 6147)
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| fixed length control field | 03037nam a22002057a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240217140542.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240217b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781032105710 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 519.54 |
| Item number | AIT |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Aitkin, Murray |
| 245 ## - TITLE STATEMENT | |
| Title | Introduction to statistical modelling and inference |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher, distributor, etc. | CRC Press |
| Place of publication, distribution, etc. | Boca Raton |
| Date of publication, distribution, etc. | 2023 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xvi, 374 p. |
| 365 ## - TRADE PRICE | |
| Price type code | GBP |
| Price amount | 82.99 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | The complexity of large-scale data sets (“Big Data”) has stimulated the development of advanced<br/>computational methods for analysing them. There are two different kinds of methods to aid this. The<br/>model-based method uses probability models and likelihood and Bayesian theory, while the model-free<br/>method does not require a probability model, likelihood or Bayesian theory. These two approaches<br/>are based on different philosophical principles of probability theory, espoused by the famous<br/>statisticians Ronald Fisher and Jerzy Neyman.<br/>Introduction to Statistical Modelling and Inference covers simple experimental and survey designs,<br/>and probability models up to and including generalised linear (regression) models and some<br/>extensions of these, including finite mixtures. A wide range of examples from different application<br/>fields are also discussed and analysed. No special software is used, beyond that needed for maximum<br/>likelihood analysis of generalised linear models. Students are expected to have a basic<br/>mathematical background in algebra, coordinate geometry and calculus.<br/>Features<br/>• Probability models are developed from the shape of the sample empirical cumulative distribution<br/>function (cdf) or a transformation of it.<br/>• Bounds for the value of the population cumulative distribution function are obtained from the<br/>Beta distribution at each point of the empirical cdf.<br/>• Bayes’s theorem is developed from the properties of the screening test for a rare condition.<br/>• The multinomial distribution provides an always-true model for any randomly sampled data.<br/>• The model-free bootstrap method for finding the precision of a sample estimate has a model-based<br/>parallel – the Bayesian bootstrap – based on the always-true multinomial distribution.<br/>• The Bayesian posterior distributions of model parameters can be obtained from the maximum<br/>likelihood analysis of the model.<br/><br/>This book is aimed at students in a wide range of disciplines including Data Science. The book is<br/>based on the model-based theory, used widely by scientists in many fields, and compares it, in less<br/>detail, with the model-free theory, popular in computer science, machine learning and official<br/>survey analysis. The development of the model-based theory is accelerated by recent developments<br/>in Bayesian analysis.<br/><br/>(https://www.routledge.com/Introduction-to-Statistical-Modelling-and-Inference/Aitkin/p/book/9781032105710) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Statistical computing |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Multivariate statistics |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Statistics and Probability |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Book |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Bill No | Bill Date | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Total Checkouts | Full call number | Accession Number | Date last seen | Copy number | Cost, replacement price | Price effective from | Koha item type |
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| Dewey Decimal Classification | Operations Management & Quantitative Techniques | SBHPL/INV/1162/2023-2024 | 27-01-2024 | Indian Institute of Management LRC | Indian Institute of Management LRC | General Stacks | 02/17/2024 | Sarat Book House Pvt. Ltd. | 5820.50 | 519.54 AIT | 005912 | 02/17/2024 | 1 | 8954.62 | 02/17/2024 | Book |