28 Best Books on Estimation Theory

“Statistical Decision Theory” Book Review: This book is a useful resource for understanding key theories and mathematical techniques in decision theory and solving statistical problems. It offers a collection of statistical procedures and quality principles for establishing optimality. The book discusses theoretical ideas and techniques, such as Utility and Loss, Prior Information and Subjective Probability, Bayesian Analysis, Minimax Analysis, Invariance, Complete and Essentially Complete Classes. It explains not only what to do, but why and where to apply the concepts. The book focuses mainly on the Bayesian viewpoint, providing practical applications for various situations.

“Mathematical Statistics : A Decision Theoretic Approach” Book Review: This book introduces and explains key concepts and methods in mathematical statistics with a theoretical approach. It mainly focuses on solving statistical problems from a decision-theoretic viewpoint. The book covers topics like Game Theory and Decision Theory, Distributions and Sufficient Statistics, Testing Hypotheses, Multiple Decision Problems, and Sequential Decision. It aims to present the fundamentals of the subject and investigate the use of decision theory in solving problems of mathematical statistics.

“Item Response Theory: Parameter Estimation Techniques” Book Review: This book is helpful for readers who have limited knowledge of educational measurement and psychometrics. It introduces the basic concepts of item response theory without the need for tedious calculations. The book covers topics like the item characteristics curve, grading, and nominally scored items, and also explains how to use R for preparing graphical presentations. The content is clear and concise, presented in a simple way to boost the reader’s confidence. The book is suitable for those who want to learn more about item response theory and its applications in educational measurement.

“Multivariate Density Estimation: Theory, Practice, and Visualization”Book Review: This concise book provides clear explanations of key topics with updated materials. It focuses on estimation techniques and methods that can be used in big data. The book emphasizes defining optimal nonparametric estimators and includes updated graphics and visualization to clarify the topics. The numerous problems at the end of each chapter help readers develop a firm grip on the topics. Ideal for theoretical and applied statisticians, practicing engineers, and readers interested in the theoretical aspects of nonparametric estimation and applications. Topics include the Representation and Geometry of Multivariate Data, Nonparametric Estimation Criteria, Histograms: Theory and Practice, Frequency Polygons, Averaged Shifted Histograms, Kernel Density Estimators, the Curse of Dimensionality and Dimension Reduction, Nonparametric Regression and Additive Models, along with appendices.

“Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory” Book Review: This is a comprehensive textbook on estimation theory for signal processing. The book covers a range of topics including probability theory, random processes, parameter estimation, linear and nonlinear estimation, and time series analysis. Each chapter includes exercises and problems to reinforce learning. This is an essential text for graduate students and researchers in electrical engineering and signal processing, as well as practitioners in the field.

“Lessons in Estimation Theory for Signal Processing, Communications, and Control” Book Review: This book presents a wealth of information in a language that is easy to understand, making it a valuable resource for students. It covers theory comprehensively, catering to the needs of various engineering branches. The book helps readers differentiate between the diverse collection of estimation methods and algorithms. The chapters cover fundamental concepts like The Linear Model, Least-Squares Estimation, Large and Small Sample Properties of Estimators, and advanced topics like Mean-Squared Estimation of Random Parameters, Maximum a Posteriori Estimation of Random Parameters, and State Estimation. It also includes Sufficient Statistics and Statistical Estimation of Parameters, Estimation and Applications of Higher-Order Statistics, and Introduction to State-Variable Models and Methods.

“Estimation with Applications to Tracking and Navigation” Book Review: This textbook provides a comprehensive coverage of state estimation algorithms used in tracking and navigation, with a focus on real-world applications. Written in an accessible language, this book covers topics such as linear and nonlinear estimation, kinematic models, computational aspects of estimation, and adaptive estimation. It balances mathematical techniques with probability and statistics, offering a detailed treatment of the subject while providing an overview of the basic concepts. This book is ideal for graduate engineering students and practicing engineers who work in tracking and navigation.

“Detection and Estimation Theory” Book Review: This engaging book presents concepts in a clear and modernized style, updated to reflect the latest advancements in the field. The focus is on discrete signal processing, while continuous signal presentations are included for consistency. Consistent notation is introduced throughout, and no prior knowledge of MATLAB is required. Aimed at practicing engineers, the book covers topics such as probability review, stochastic processes, signal representations and statistics, single and multiple sample detection of binary hypotheses, detection of signals with random parameters, and more. It also covers fundamentals of estimation theory, distribution-free estimation techniques, detection and estimation in non-Gaussian noise systems, multi-user detection, and much more. The book includes multiple appendices for further understanding.

“Tool Kits in Regional Science: Theory, Models, and Estimation (Advances in Spatial Science)” Book Review: This book is intended for students and covers topics like equilibrium models, nonlinear dynamics, neural modeling, and innovation. It covers socio-economic systems, socio-spatial dynamics, and non-linear probabilistic change, and also focuses on the principles of neural spatial interaction. It’s designed to be easy to understand and helpful for students who want to learn more about these topics.

“Stochastic Processes, Estimation, and Control (Advances in Design and Control)” Book Review: This book is intended for graduate students and covers topics like probability theory and stochastic optimal control, as well as the practical applications of dynamic programming for both discrete-time and continuous-time systems. It offers a broad range of approaches, helping readers learn how to formulate various problems using mathematical optimization techniques. It’s designed to be helpful for those studying the subject and focuses on both theoretical and practical aspects.

“Advances in Estimation, Navigation, and Spacecraft Control” Book Review: The main audience for this book is students of aerospace engineering, researchers, and scientists. It primarily covers topics related to estimation, navigation, and spacecraft control, and is divided into 27 chapters that can be further broken down into three parts: estimation, navigation, and spacecraft guidance, navigation, and control. It’s designed to be comprehensive and helpful for those studying or working in the field of aero-space engineering.

“Robust Estimation and Failure Detection: A Concise Treatment (Advances in Industrial Control)” Book Review: This book is meant for engineering students and focuses on robust estimation and failure detection, including theories like Kalman filtering and H-infinity filtering. It helps students understand methods for detecting failures and designing failure detectors that are sensitive to failures but not model variations. The book also covers how to design failure detectors that can handle different scenarios. In summary, the book is a great resource for students who want to learn about failure detection and estimation.

“Parallel Algorithms for Linear Models: Numerical Methods and Estimation Problems (Advances in Computational Economics)” Book Review: This book is about designing and analyzing parallel algorithms for solving linear models. It is split into two parts: the first part has four chapters, which focus on computational aspects of solving linear models. The second part has two chapters and is all about numerical methods for seemingly unrelated regression equations (SURE) and simultaneous equations models. The book is helpful for those who are interested in parallel computing and want to learn more about linear models and their solutions.

“Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB” Book Review: This is a textbook for engineering students and professionals. It provides a practical approach to the subject by using MATLAB to solve problems. The book covers various topics, including classification, parameter estimation, and state estimation. It includes a comprehensive introduction to pattern recognition and machine learning, Bayesian parameter estimation, Kalman filtering, and many more. Overall, it is a useful resource for anyone looking to gain a deeper understanding of these topics in an engineering context.

“Statistical Inference: Theory of Estimation” Book Review: This book is for postgraduate students and covers theorems and results on uniformly minimum variance unbiased estimators, as well as different methods of estimation like maximum likelihood, consistency, and best asymptotic normality. It also includes chapters on Pitman estimator, scale models, symmetry structure, empirical Bayes, and more. The book contains solved examples and problems with answers at the end of each chapter. It’s useful for students, researchers, and scientists who want to learn about unbiased estimation, Bayesian and minimax estimation, and the Rao and Blackwell theorem, Lehmann-Scheffe theorem, and other related principles.

“Model Based Parameter Estimation: Theory and Applications (Contributions in Mathematical and Computational Sciences)” Book Review: This book provides a comprehensive overview of parameter estimation techniques based on mathematical modeling. It covers topics like model-based parameter estimation and optimization, parameter identification, optimal experiment design, applications of parameter estimation in various fields like chemical engineering, medicine, and economics. The book also includes case studies and practical examples. The authors, Hans Georg Bock and Thomas Carraro, have compiled a valuable resource for graduate students and researchers in the field of applied mathematics and engineering.

“Estimating and Costing in Civil Engineering: Theory and Practice Including Specifications and Valuations” Book Review: This book is for civil engineering students, and it covers both the theory and practical applications. It includes conversion tables, technical data, and other useful information. The book discusses drawing and design formats often used in civil engineering, as well as analyzing rates, specifications, and filing tender documentation. It’s a helpful resource for those learning about civil engineering concepts and applications and includes practical examples that could be useful in real-world scenarios.

“Detection, Estimation and Modulation Theory – Part III” Book Review: This is a comprehensive textbook on signal processing. It covers topics such as statistical signal processing, estimation theory, detection theory, and modulation theory. The book is well-written and includes numerous examples and exercises to aid in the learning process. The chapters cover a wide range of topics, including linear and nonlinear estimation, detection of signals in noise, and adaptive filtering.

“Fundamentals of Statistical Processing, Volume I: Estimation Theory (Prentice-Hall Signal Processing Series)” Book Review: This book is for students of biomedical engineering, radar engineering, and physics. It covers the fundamentals of statistical signal processing and focuses on designing and implementing signal processing algorithms. The book helps with designing and analyzing signal processing systems and includes solved examples, exercises, and short questions at the end of each chapter. It’s helpful for those interested in these specific areas of engineering and physics and includes practical applications that could be useful in real-life scenarios.

“Theory of Point Estimation (Springer Texts in Statistics)” Book Review: This is a comprehensive textbook on the mathematical theory of statistical point estimation. It covers the fundamental concepts of point estimation, including unbiased and minimum variance estimators, the Cramér-Rao inequality, and the method of moments. The book also covers advanced topics, such as large-sample theory, Bayesian estimation, and asymptotic properties of estimators. The text includes detailed proofs of important results and examples to illustrate the theory. This book is suitable for graduate students and researchers in statistics and related fields.

“Statistical Theory: A Concise Introduction” Book Review: This book is for biomedical engineering students, researchers, and scientists. It covers major statistical concepts like parameter estimation and hypothesis testing, and includes theorems, results, and proofs of statistical theory. It’s focused on the theoretical concepts used in statistical tools of linear regression. The book includes solved examples, exercises, and short questions at the end of each chapter. It’s helpful for those interested in these specific areas of research and includes practical applications that could be useful in real-life scenarios.

“Statistical Decision Theory: Estimation, Testing, and Selection (Springer Series in Statistics)” Book Review: This book is for students, researchers, and scientists interested in statistical decision theory and modern asymptotic decision theory. It covers topics such as Bayes theorem, Bayes statistical theory, and lecam’s theory. It includes theoretical portions as well as practical applications. The book consists of solved examples, exercises, and proofs at the end of each chapter. It’s helpful for those interested in these specific areas of research and includes practical applications that could be useful in real-life scenarios.

“Mathematical and Statistical Estimation Approaches in Epidemiology” Book Review: This book is designed for students, researchers, and scientists and explores the relationship between models and disease. It emphasizes the application of mathematical and statistical methods in epidemiology, covering both theoretical and practical aspects of disease analysis. Each chapter includes exercises, examples, and short questions to reinforce the concepts discussed. This book provides an in-depth examination of estimation approaches in epidemiology using mathematical and statistical techniques.

“Handbook of Fitting Statistical Distributions with R” Book Review: This book is intended for students, researchers, and scientists who seek information on advanced applications. It presents real-world examples of water systems, inventory management, insurance, and materials science. It concentrates on applications in agriculture and reliability estimation. It also covers the methodology and applications of fitting statistical distributions. Solved examples, exercises, and short questions at the end of each chapter are included in this book.

“Automotive Engines: Control, Estimation, Statistical Detection” Book Review: This book is intended for mechanical engineering students, researchers, and scientists. It explores speed control, cylinder flow estimation, engine torque, and friction estimation, all of which are techniques relevant to automotive engine control. The book covers topics such as input estimation, composite adaptation, and threshold detection adaptation. Additionally, it features practical applications of automotive engines, in addition to theoretical concepts.

“Fundamentals of Statistical Signal Processing, Volume II: Detection Theory: 002 (Prentice-Hall Signal Processing Series)” Book Review: This book targets students, researchers, and scientists of electrical engineering. It covers the fundamental principles of statistical processing and includes chapters on the Neyman-Pearson theorem and handling irrelevant data. It also explores Bayes risk and multiple hypothesis testing, as well as non-Gaussian noise and deterministic signals. Each chapter includes solved examples, exercises, and short questions to reinforce the concepts.

“Statistical Decision Theory (SpringerBriefs in Statistics)” Book Review: This is a concise and comprehensive book on statistical decision theory. The book is ideal for students and researchers in statistics and related fields. The book covers the basic principles of statistical decision theory and its applications in real-world scenarios. The topics include risk functions, Bayesian decision theory, estimation theory, hypothesis testing, and game theory. The book also includes numerous examples, exercises, and problems at the end of each chapter.