Welcome to Loot.co.za!
Sign in / Register |Wishlists & Gift Vouchers |Help | Advanced search
|
Your cart is empty |
|||
Showing 1 - 25 of 34 matches in All Departments
Artificial Intelligence, Volume 49 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics. Chapters in this new release include AI Teacher-Student based Adaptive Structural Deep Learning Model and Its Estimating Uncertainty of Image Data, Machine-derived Intelligence: Computations Beyond the Null Hypothesis, Object oriented basis of artificial intelligence methodologies I in Judicial Systems in India, Artificial Intelligence in Systems Biology, Machine-Learning in Geometry and Physics, Innovation and Machine Learning: Crowdsourcing Open-Source Natural Language Processing (NLP) Algorithms to Advance Public Health Surveillance, and more. Other chapters cover Learning and identity testing of Markov chains, Data privacy for machine learning and statistics, and The interface between AI and Mathematics.
Integrated Population Biology and Modeling: Part B, Volume 40, offers very delicately complex and precise realities of quantifying modern and traditional methods of understanding populations and population dynamics, with this updated release focusing on Prey-predator animal models, Back projections, Evolutionary Biology computations, Population biology of collective behavior and bio patchiness, Collective behavior, Population biology through data science, Mathematical modeling of multi-species mutualism: new insights, remaining challenges and applications to ecology, Population Dynamics of Manipur, Stochastic Processes and Population Dynamics Models: The Mechanisms for Extinction, Persistence and Resonance, Theories of Stationary Populations and association with life lived and life left, and more.
Essential Methods for Design Based Sample Surveys presents key method contributions selected from the volume in the Handbook of Statistics: Sample Surveys: Design, Methods and Applications, Vol. 29a (2009). This essential reference provides specific aspects of sample survey design, with references to important contributions and available software. The content is aimed at researchers and practitioners who use statistical methods in design based sample surveys and market research. This book presents the core essential methods of sample selection and data processing. The data processing discussion covers editing and imputation, and methods of disclosure control. This reference contains a large variety of applications in specialized areas such as household and business surveys, marketing research, opinion polls and censuses.
Statistical learning and analysis techniques have become extremely important today, given the tremendous growth in the size of heterogeneous data collections and the ability to process it even from physically distant locations. Recent advances made in the field of machine learning provide a strong framework for robust learning from the diverse corpora and continue to impact a variety of research problems across multiple scientific disciplines. The aim of this handbook is to familiarize beginners as well as experts with some of the recent techniques in this field. The Handbook is divided in two sections: Theory and
Applications, covering machine learning, data analytics,
biometrics, document recognition and security. emphasis on applications-oriented techniques
The field of statistics not only affects all areas of scientific activity, but also many other matters such as public policy. It is branching rapidly into so many different subjects that a series of handbooks is the only way of comprehensively presenting the various aspects of statistical methodology, applications, and recent developments. The "Handbook of Statistics" is a series of self-contained reference books. Each volume is devoted to a particular topic in statistics, with Volume 30 dealing with time series. The series is addressed to the entire community of statisticians and scientists in various disciplines who use statistical methodology in their work. At the same time, special emphasis is placed on applications-oriented techniques, with the applied statistician in mind as the primary audience. Comprehensively presents the various aspects of statistical methodologyDiscusses a wide variety of diverse applications and recent developmentsContributors are internationally renowened experts in their respective areas
This accessible reference includes selected contributions from Bayesian Thinking - Modeling and Computation, Volume 25 in the Handbook of Statistics Series, with a focus on key methodologies and applications for Bayesian models and computation. It describes parametric and nonparametric Bayesian methods for modeling, and how to use modern computational methods to summarize inferences using simulation. The book covers a wide range of topics including objective and subjective Bayesian inferences, with a variety of applications in modeling categorical, survival, spatial, spatiotemporal, Epidemiological, small area and micro array data. Aids critical thinking on causal effects
This new handbook contains the most comprehensive account of sample surveys theory and practice to date. It is a second volume on sample surveys, with the goal of updating and extending the sampling volume published as volume 6 of the Handbook of Statistics in 1988. The present handbook is divided into two volumes (29A and 29B), with a total of 41 chapters, covering current developments in almost every aspect of sample surveys, with references to important contributions and available software. It can serve as a self contained guide to researchers and practitioners, with appropriate balance between theory and real life applications. Each of the two volumes is divided into three parts, with each
part preceded by an introduction, summarizing the main developments
in the areas covered in that part. Volume29A deals with methods of
sample selection and data processing, with the later including
editing and imputation, handling of outliers and measurement
errors, and methods of disclosure control. The volume contains also
a large variety of applications in specialized areas such as
household and business surveys, marketing research, opinion polls
and censuses. Volume29B is concerned with inference, distinguishing
between design-based and model-based methods and focusing on
specific problems such as small area estimation, analysis of
longitudinal data, categorical data analysis and inference on
distribution functions. The volume contains also chapters dealing
with case-control studies, asymptotic properties of estimators and
decision theoretic aspects.
This new handbook contains the most comprehensive account of sample surveys theory and practice to date. It is a second volume on sample surveys, with the goal of updating and extending the sampling volume published as volume 6 of the Handbook of Statistics in 1988. The present handbook is divided into two volumes (29A and 29B), with a total of 41 chapters, covering current developments in almost every aspect of sample surveys, with references to important contributions and available software. It can serve as a self contained guide to researchers and practitioners, with appropriate balance between theory and real life applications. Each of the two volumes is divided into three parts, with each
part preceded by an introduction, summarizing the main developments
in the areas covered in that part. Volume 1 deals with methods of
sample selection and data processing, with the later including
editing and imputation, handling of outliers and measurement
errors, and methods of disclosure control. The volume contains also
a large variety of applications in specialized areas such as
household and business surveys, marketing research, opinion polls
and censuses. Volume 2 is concerned with inference, distinguishing
between design-based and model-based methods and focusing on
specific problems such as small area estimation, analysis of
longitudinal data, categorical data analysis and inference on
distribution functions. The volume contains also chapters dealing
with case-control studies, asymptotic properties of estimators and
decision theoretic aspects. Comprehensive account of recent developments in sample survey theory and practice Covers a wide variety of diverse applications Comprehensive bibliography
Longitudinal data consists of a series of repeated observations of the same subjects over an extended time frame, and is thus useful for measuring change. Such studies arise in a variety of fields, such as health sciences, genomic studies, experimental physics, sociology, sports and student enrollment in universities. Many challenges can arise in longitudinal studies. For example, in health studies, intra-subject correlation of responses must be accounted for, covariates can vary with time, and bias can arise due to patients dropping out of the study. This volume under development brings recent and classical works in the modeling and analysis of longitudinal data and their utility for improving society.
Integrated Population Biology and Modeling: Part A offers very complex and precise realities of quantifying modern and traditional methods of understanding populations and population dynamics. Chapters cover emerging topics of note, including Longevity dynamics, Modeling human-environment interactions, Survival Probabilities from 5-Year Cumulative Life Table Survival Ratios (Tx+5/Tx): Some Innovative Methodological Investigations, Cell migration Models, Evolutionary Dynamics of Cancer Cells, an Integrated approach for modeling of coastal lagoons: A case for Chilka Lake, India, Population and metapopulation dynamics, Mortality analysis: measures and models, Stationary Population Models, Are there biological and social limits to human longevity?, Probability models in biology, Stochastic Models in Population Biology, and more.
Deep Learning, Volume 48 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics, including Generative Adversarial Networks for Biometric Synthesis, Data Science and Pattern Recognition, Facial Data Analysis, Deep Learning in Electronics, Pattern Recognition, Computer Vision and Image Processing, Mechanical Systems, Crop Technology and Weather, Manipulating Faces for Identity Theft via Morphing and Deepfake, Biomedical Engineering, and more.
Advancements in Bayesian Methods and Implementation, Volume 47 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics, including Fisher Information, Cramer-Rao and Bayesian Paradigm, Compound beta binomial distribution functions, MCMC for GLMMS, Signal Processing and Bayesian, Mathematical theory of Bayesian statistics where all models are wrong, Machine Learning and Bayesian, Non-parametric Bayes, Bayesian testing, and Data Analysis with humans, Variational inference or Functional horseshoe, Generalized Bayes.
Geometry and Statistics, Volume 46 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters written by an international board of authors.
The subject of information geometry blends several areas of statistics, computer science, physics, and mathematics. The subject evolved from the groundbreaking article published by legendary statistician C.R. Rao in 1945. His works led to the creation of Cramer-Rao bounds, Rao distance, and Rao-Blackawellization. Fisher-Rao metrics and Rao distances play a very important role in geodesics, econometric analysis to modern-day business analytics. The chapters of the book are written by experts in the field who have been promoting the field of information geometry and its applications.
This book focuses on dealing with large-scale data, a field
commonly referred to as data mining. The book is divided into three
sections. The first deals with an introduction to statistical
aspects of data mining and machine learning and includes
applications to text analysis, computer intrusion detection, and
hiding of information in digital files. The second section focuses
on a variety of statistical methodologies that have proven to be
effective in data mining applications. These include clustering,
classification, multivariate density estimation, tree-based
methods, pattern recognition, outlier detection, genetic
algorithms, and dimensionality reduction. The third section focuses
on data visualization and covers issues of visualization of
high-dimensional data, novel graphical techniques with a focus on
human factors, interactive graphics, and data visualization using
virtual reality. This book represents a thorough cross section of
internationally renowned thinkers who are inventing methods for
dealing with a new data paradigm.
This text presents the 17th and concluding volume of the "Statistics Handbook". It covers order statistics, dealing primarily with applications. The book is divided into six parts as follows: results for specific distributions; linear estimation; inferential methods; prediction; goodness-of-fit tests; and applications. Theoretical advances have been made in this area of research, and order statistics has also found important applications in many diverse areas, these include life-testing and reliability, robustness studies, statistical quality control, filtering theory, signal processing, image processing, and radar target detection. A variety of theoretical researchers, statisticians and engineers have been brought together to produce this handbook, and the subject of order statistics has been split across volumes 16 and 17. Volume 17 focuses on applications and an extensive author and subject index aims to offer easy access to all the material included in both volumes.
Data Science: Theory and Applications, Volume 44 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of interesting topics, including Modeling extreme climatic events using the generalized extreme value distribution, Bayesian Methods in Data Science, Mathematical Modeling in Health Economic Evaluations, Data Science in Cancer Genomics, Blockchain Technology: Theory and Practice, Statistical outline of animal home ranges, an application of set estimation, Application of Data Handling Techniques to Predict Pavement Performance, Analysis of individual treatment effects for enhanced inferences in medicine, and more. Additional sections cover Nonparametric Data Science: Testing Hypotheses in Large Complex Data, From Urban Mobility Problems to Data Science Solutions, and Data Structures and Artificial Intelligence Methods.
"C. R. Rao would be found in almost any statistician’s list of five outstanding workers in the world of Mathematical Statistics today. His book represents a comprehensive account of the main body of results that comprise modern statistical theory." "[C. R. Rao is] one of the pioneers who laid the foundations of statistics which grew from ad hoc origins into a firmly grounded mathematical science." Translated into six major languages of the world, C. R. Rao’s Linear Statistical Inference and Its Applications is one of the foremost works in statistical inference in the literature. Incorporating the important developments in the subject that have taken place in the last three decades, this paperback reprint of his classic work on statistical inference remains highly applicable to statistical analysis. Presenting the theory and techniques of statistical inference in a logically integrated and practical form, it covers:
Written for the student and professional with a basic knowledge of statistics, this practical paperback edition gives this industry standard new life as a key resource for practicing statisticians and statisticians-in-training.
Conceptual Econometrics Using R, Volume 41 provides state-of-the-art information on important topics in econometrics, including quantitative game theory, multivariate GARCH, stochastic frontiers, fractional responses, specification testing and model selection, exogeneity testing, causal analysis and forecasting, GMM models, asset bubbles and crises, corporate investments, classification, forecasting, nonstandard problems, cointegration, productivity and financial market jumps and co-jumps, among others.
Data Gathering, Analysis and Protection of Privacy through Randomized Response Techniques: Qualitative and Quantitative Human Traits tackles how to gather and analyze data relating to stigmatizing human traits. S.L. Warner invented RRT and published it in JASA, 1965. In the 50 years since, the subject has grown tremendously, with continued growth. This book comprehensively consolidates the literature to commemorate the inception of RR.
Financial, Macro and Micro Econometrics Using R, Volume 42, provides state-of-the-art information on important topics in econometrics, including multivariate GARCH, stochastic frontiers, fractional responses, specification testing and model selection, exogeneity testing, causal analysis and forecasting, GMM models, asset bubbles and crises, corporate investments, classification, forecasting, nonstandard problems, cointegration, financial market jumps and co-jumps, among other topics.
Handbook of Statistics: Disease Modelling and Public Health, Part B, Volume 37 addresses new challenges in existing and emerging diseases. As a two part volume, this title covers an extensive range of techniques in the field, with this book including chapters on Reaction diffusion equations and their application on bacterial communication, Spike and slab methods in disease modeling, Mathematical modeling of mass screening and parameter estimation, Individual-based and agent-based models for infectious disease transmission and evolution: an overview, and a section on Visual Clustering of Static and Dynamic High Dimensional Data. This volume covers the lack of availability of complete data relating to disease symptoms and disease epidemiology, one of the biggest challenges facing vaccine developers, public health planners, epidemiologists and health sector researchers.
R is open source statistical computing software. Since the R core group was formed in 1997, R has been extended by a very large number of packages with extensive documentation along with examples freely available on the internet. It offers a large number of statistical and numerical methods and graphical tools and visualization of extraordinarily high quality. R was recently ranked in 14th place by the Transparent Language Popularity Index and 6th as a scripting language, after PHP, Python, and Perl. The book is designed so that it can be used right away by novices while appealing to experienced users as well. Each article begins with a data example that can be downloaded directly from the R website. Data analysis questions are articulated following the presentation of the data. The necessary R commands are spelled out and executed and the output is presented and discussed. Other examples of data sets with a different flavor and different set of commands but following the theme of the article are presented as well. Each chapter predents a hands-on-experience. R has superb graphical outlays and the book brings out the essentials in this arena. The end user can benefit immensely by applying the graphics to enhance research findings. The core statistical methodologies such as regression, survival analysis, and discrete data are all covered.
Cognitive Computing: Theory and Applications, written by internationally renowned experts, focuses on cognitive computing and its theory and applications, including the use of cognitive computing to manage renewable energy, the environment, and other scarce resources, machine learning models and algorithms, biometrics, Kernel Based Models for transductive learning, neural networks, graph analytics in cyber security, neural networks, data driven speech recognition, and analytical platforms to study the brain-computer interface.
This volume, representing a compilation of authoritative reviews on
a multitude of uses of statistics in epidemiology and medical
statistics written by internationally renowned experts, is
addressed to statisticians working in biomedical and
epidemiological fields who use statistical and quantitative methods
in their work. While the use of statistics in these fields has a
long and rich history, explosive growth of science in general and
clinical and epidemiological sciences in particular have gone
through a see of change, spawning the development of new methods
and innovative adaptations of standard methods. Since the
literature is highly scattered, the Editors have undertaken this
humble exercise to document a representative collection of topics
of broad interest to diverse users. The volume spans a cross
section of standard topics oriented toward users in the current
evolving field, as well as special topics in much need which have
more recent origins. This volume was prepared especially keeping
the applied statisticians in mind, emphasizing
applications-oriented methods and techniques, including references
to appropriate software when relevant. |
You may like...
|