Search results for "survival analysis with python"
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Handbook of Capture-Recapture Analysis
Every day, biologists in parkas, raincoats, and rubber boots go into the field to capture and mark a variety of animal species. Back in the office, statisticians create analytical models for the field biologists' data. But many times, representatives of the two professions do not fully understand one another's roles. This book bridges this gap by helping biologists understand state-of-the-art statistical methods for analyzing capture-recapture data. In so doing, statisticians will also become more familiar with the design of field studies and with the real-life issues facing biologists. Reliable outcomes of capture-recapture studies are vital to answering key ecological questions. Is the population increasing or decreasing? Do more or fewer animals have a particular characteristic? In answering these questions, biologists cannot hope to capture and mark entire populations. And frequently, the populations change unpredictably during a study. Thus, increasingly sophisticated models have been employed to convert data into answers to ecological questions.This book, by experts in capture-recapture analysis, introduces the most up-to-date methods for data analysis while explaining the theory behind those methods. Thorough, concise, and portable, it will be immensely useful to biologists, biometricians, and statisticians, students in both fields, and anyone else engaged in the capture-recapture process.
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Forest Analytics with R
Forest Analytics with R combines practical, down-to-earth forestry data analysis and solutions to real forest management challenges with state-of-the-art statistical and data-handling functionality. Theauthors adopt a problem-driven approach, in which statistical and mathematical tools are introduced in the context of the forestry problem that they can help to resolve. All the tools are introduced in the context of real forestry datasets, which provide compelling examples of practical applications.The modeling challenges covered within the book include imputation and interpolation for spatial data, fitting probability density functions to tree measurement data using maximum likelihood, fitting allometric functions using both linear and non-linear least-squares regression, and fitting growth models using both linear and non-linear mixed-effects modeling. The coverage also includes deploying andusing forest growth models written in compiled languages, analysis of natural resources and forestry inventory data, and forest estate planning and optimization using linear programming. The book would be ideal for a one-semester class in forest biometrics or applied statistics for natural resources management. The text assumes no programming background, some introductory statistics,and very basic applied mathematics.
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Survival April–May 2021: Facing Russia
Survival, the IISS’s bimonthly journal, challenges conventional wisdom and brings fresh, often controversial, perspectives on strategic issues of the moment.In this issue:Marcus Willett examines Russia’s SolarWinds hack and the painful lessons it holds for the United StatesIn a special forum, seven experts consider the purpose and effectiveness of the United Nations, as well as the intricacies of Security Council reform, on its 75th anniversaryPeter Rudolf argues that the United States’ perception of China as a revisionist strategic rival will complicate transatlantic policy coordinationRay Takeyh considers whether the US intelligence community bungled assessments of Iran’s political stability in the late 1970sAnd six more thought-provoking pieces, as well as our regular Book Reviews and Noteworthy column.Editor: Dr Dana AllinManaging Editor: Jonathan StevensonAssociate Editor: Carolyn WestAssistant Editor: Jessica Watson
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Statistical Analysis of Epidemiologic Data
Analytic procedures suitable for the study of human disease are scattered throughout the statistical and epidemiologic literature. Explanations of their properties are frequently presented in mathematical and theoretical language. This well-established text gives readers a clear understanding of the statistical methods that are widely used in epidemiologic research without depending on advanced mathematical or statistical theory. By applying these methods to actualdata, Selvin reveals the strengths and weaknesses of each analytic approach. He combines techniques from the fields of statistics, biostatistics, demography and epidemiology to present a comprehensive overview that does not require computational details of the statistical techniques described.For the Third Edition, Selvin took out some old material (e.g. the section on rarely used cross-over designs) and added new material (e.g. sections on frequently used contingency table analysis). Throughout the text he enriched existing discussions with new elements, including the analysis of multi-level categorical data and simple, intuitive arguments that exponential survival times cause the hazard function to be constant. He added a dozen new applied examples to illustrate such topics as thepitfalls of proportional mortality data, the analysis of matched pair categorical data, and the age-adjustment of mortality rates based on statistical models. The most important new feature is a chapter on Poisson regression analysis. This essential statistical tool permits the multivariableanalysis of rates, probabilities and counts.
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Artificial Intelligence with Python
Entering the field of artificial intelligence and data science can seem daunting to beginners with little to no prior background, especially those with no programming experience. The concepts used in self-driving cars and virtual assistants like Amazon’s Alexa may seem very complex and difficult to grasp. The aim of Artificial Intelligence in Python is to make AI accessible and easy to understand for people with little to no programming experience though practical exercises. Newcomers will gain the necessary knowledge on how to create such systems, which are capable of executing tasks that require some form of human-like intelligence.This book introduces readers to various topics and examples of programming in Python, as well as key concepts in artificial intelligence. Python programming skills will be imparted as we go along. Concepts and code snippets will be covered in a step-by-step manner, to guide and instill confidence in beginners. Complex subjects in deep learning and machine learning will be broken down into easy-to-digest content and examples. Artificial intelligence implementations will also be shared, allowing beginners to generate their own artificial intelligence algorithms for reinforcement learning, style transfer, chatbots, speech, and natural language processing.
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Mathematical Logic through Python
Using a unique pedagogical approach, this text introduces mathematical logic by guiding students in implementing the underlying logical concepts and mathematical proofs via Python programming. This approach, tailored to the unique intuitions and strengths of the ever-growing population of programming-savvy students, brings mathematical logic into the comfort zone of these students and provides clarity that can only be achieved by a deep hands-on understanding and the satisfaction of having created working code. While the approach is unique, the text follows the same set of topics typically covered in a one-semester undergraduate course, including propositional logic and first-order predicate logic, culminating in a proof of Gödel's completeness theorem. A sneak peek to Gödel's incompleteness theorem is also provided. The textbook is accompanied by an extensive collection of programming tasks, code skeletons, and unit tests. Familiarity with proofs and basic proficiency in Python is assumed.
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Artificial Intelligence with Python
Entering the field of artificial intelligence and data science can seem daunting to beginners with little to no prior background, especially those with no programming experience. The concepts used in self-driving cars and virtual assistants like Amazon’s Alexa may seem very complex and difficult to grasp. The aim of Artificial Intelligence in Python is to make AI accessible and easy to understand for people with little to no programming experience though practical exercises. Newcomers will gain the necessary knowledge on how to create such systems, which are capable of executing tasks that require some form of human-like intelligence.This book introduces readers to various topics and examples of programming in Python, as well as key concepts in artificial intelligence. Python programming skills will be imparted as we go along. Concepts and code snippets will be covered in a step-by-step manner, to guide and instill confidence in beginners. Complex subjects in deep learning and machine learning will be broken down into easy-to-digest content and examples. Artificial intelligence implementations will also be shared, allowing beginners to generate their own artificial intelligence algorithms for reinforcement learning, style transfer, chatbots, speech, and natural language processing.
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Statistical Analysis of Behavioural Data
Presenting sophisticated statistical methods for analysing continuous-time records of behaviour, this book integrates recent developments in ethology, mathematical modelling, statistics, and technology. With numerous worked examples, the book leads the reader through the mathematical processes and their applications. The book will be incomparably useful to students and researchers in all areas of behavioural science.The authors show how to analyse behavioural data starting with a basic model, the continuous-time Markov chain. They then indicate how and when this model can be generalized and demonstrate the suitability of their approach for detecting, for example, the effects of different experimental treatments or of changes in the social or physical environment. Competitive interactions such as predator-prey or host-parasite are also good subjects for this type of analysis.
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Programming with Python
This book is an introduction to Python Programming and provides a practical approach to the subject. The basic concepts of Python are explained in detail and augmented with examples and diagrams for a thorough understanding of the subject.The book is primarily aimed at students with little or no prior knowledge of programming languages. However, self-taught and hobbyist programmers, scientists, engineers, computing professionals and computer scientists and others who need to program as part of their work may also use this book for understanding the basic concepts of Python.Print edition not for sale in South Asia (India, Sri Lanka, Nepal, Bangladesh, Pakistan or Bhutan)
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Python for non-Pythonians
The book uses a very simple and accessible language. All the descriptions of Python functionalities come with intuitive examples to make you learn by doing. This is not a theoretical book and does not cover some of the most internal features of Python. The intention of the authors is to allow business oriented people to start using Python. The main reason for such a choice of style is due to the increasing number of requests by non-technical professionals to solve daily problems and tasks. Whether we want to append multiple spreadsheets or profile the customer base, being able to use a solid infrastructure which enables to collect, check, process, analyze data, and report results has become a basic requirement in most industries. The structure of this book replicates the structure of a grammar book. It first introduces the building blocks of Python - i.e., objects - which can be compared to the different types of words (e.g., verbs, nouns) of a language. It introduces readers to the syntax rules of Python - i.e., statements, functions, and classes. It then provides introduces how to use this new language for managing data. One thing that we found particularly helpful for our students is to provide wrong Python codes with the error message that Python provides, and ask them to fix the code in order to make it work. As making mistakes is very common, we would say normal, in programming, learning how to interpret the error messages and fix codes is extremely important from a pedagogical perspective. The book always challenges students with a series of "fix the code" questions at the end of each a topic. We adopt this structure because we found that students learn how to successfully write algorithms in Python when they understand its logic and the meaning of every single element of the algorithm. We have experimented that this natural progression, from the most basic elements to very complex scripts, raises students' self-confidence and proficiency in writing their own algorithms. Hence, the structure of the textbook greatly helps students and instructors to gradually acquire the "programming" mindset. Also, our teaching experience indicates that, as with any other language, students learn by practicing. For this reason our textbook, is rich in exercises of increasing complexity.
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WAIC and WBIC with Python Stan
Master the art of machine learning and data science by diving into the essence of mathematical logic with this comprehensive textbook. This book focuses on the widely applicable information criterion (WAIC), also described as the Watanabe-Akaike information criterion, and the widely applicable Bayesian information criterion (WBIC), also described as the Watanabe Bayesian information criterion. The book expertly guides you through relevant mathematical problems while also providing hands-on experience with programming in Python and Stan. Whether you’re a data scientist looking to refine your model selection process or a researcher who wants to explore the latest developments in Bayesian statistics, this accessible guide will give you a firm grasp of Watanabe Bayesian Theory.The key features of this indispensable book include:A clear and self-contained writing style, ensuring ease of understanding for readers at various levels of expertise.100 carefully selected exercises accompanied by solutions in the main text, enabling readers to effectively gauge their progress and comprehension.A comprehensive guide to Sumio Watanabe’s groundbreaking Bayes theory, demystifying a subject once considered too challenging even for seasoned statisticians.Detailed source programs and Stan codes that will enhance readers’ grasp of the mathematical concepts presented.A streamlined approach to algebraic geometry topics in Chapter 6, making Bayes theory more accessible and less daunting.Embark on your machine learning and data science journey with this essential textbook and unlock the full potential of WAIC and WBIC today!
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Predicting Recidivism Using Survival Models
Our interest in the statistical modeling of data on the timing of recidivism began in the mid 1970s when we were both junior members of the eco nomics department at the University of North Carolina. At that time, methods of analyzing qualitative and limited variables were being developed rapidly in the econometric literature, and we became interested in finding a suitable application for these new methods. Data on the timing of recidivism offered unique and interesting statistical challenges, such as skewness of the distribution and the presence of censoring. Being young and foolish, we decided it would be fun to try something "really" difficult. And, being young and ignorant, we were blissfully unaware of the con current developments in the statistical modeling of survival times that were then appearing in the biostatistics, operations research, and criminological literatures. In the course of some earlier research, we had learned that the North Carolina Department of Correction had an unusually well-developed data base on their inmates. We approached the Department and asked if they would be interested in working with us to develop models that would predict when their former charges would return to their custody. They agreed because they were interested in using such models to evaluate rehabilitative programs and alternative prison management systems and to help project future prison populations.
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Size and Survival
This book provides the first comparative survey of the security problems faced by the small island and enclave developing states of the two major regions in the world characterised by the presence of such states - the Caribbean and the Pacific. It both contributes to the theoretical debate about size and security and offers an up-to-date and comprehensive analysis of the politics of security in these regions.
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