Maximum Likelihood Estimation and Inference by Russell B Millar
Applied Likelihood Methods provides an accessible and practical introduction to likelihood modeling, supported by examples and software. The book features applications from a range of disciplines, including statistics, medicine, biology, and ecology.-
Multiple Imputation and its Application
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Data Monitoring Committees in Clinical Trials
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Bayesian Approaches to Clinical Trials and Health-Care Evaluation
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Risk Assessment
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Evidence Synthesis for Decision Making in Healthcare
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Modern Analysis of Customer Surveys
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Statistical Practice in Business and Industry
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Spatio-temporal Design
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Applied Missing Data Analysis in the Health Sciences
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Statistical Methods for Hospital Monitoring with R
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Statistical Methods for Trend Detection and Analysis in the Environmental Sciences
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Statistical Estimation of Epidemiological Risk
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Statistical Monitoring of Complex Multivariate Processes
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A Practical Guide to Designing Phase II Trials in Oncology
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Quantitative Finance
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Statistical Applications for Environmental Analysis and Risk Assessment
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Statistical Methods for Dose-Finding Experiments
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Geostatistics for Environmental Scientists
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Selection Bias and Covariate Imbalances in Randomized Clinical Trials
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Spatial Analysis Along Networks
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Using Statistical Methods for Water Quality Management
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Weight-of-Evidence for Forensic DNA Profiles
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Statistical and Methodological Aspects of Oral Health Research
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Statistics and the Evaluation of Evidence for Forensic Scientists
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Modeling and Analysis of Compositional Data
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Statistical DNA Forensics
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Analysing Survival Data from Clinical Trials and Observational Studies
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Statistical Analysis of Microstructures in Materials Science
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Understanding Biostatistics
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Nondetects and Data Analysis
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Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science
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Applied Mixed Models in Medicine
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How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health Related Research
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Statistical Issues in Drug Development
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Individual Participant Data Meta-Analysis
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Missing Data in Clinical Studies
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Comparing Clinical Measurement Methods
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Statistical Methods for Groundwater Monitoring
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The False Discovery Rate
“This book is well-presented and would suit applied scientists, researchers, graduate students and particularly anyone who uses likelihood and such methods to their studies and applications” (ISR, 2012)
Russell B. Millar is the author of Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB, published by Wiley.
| SKU | Unavailable |
| ISBN 13 | 9780470094822 |
| ISBN 10 | 0470094826 |
| Title | Maximum Likelihood Estimation and Inference |
| Author | Russell B Millar |
| Series | Statistics In Practice |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | John Wiley & Sons Inc |
| Year published | 2011-09-02 |
| Number of pages | 376 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |