We have compiled the list of Best Reference Books on Estimation Theory subject. These books are used by students of top universities, institutes and colleges. Here is the full list of best books on Estimation Theory along with reviews.
Kindly note that we have put a lot of effort into researching the best books on Estimation Theory subject and came out with a recommended list of best books. The table below contains the Name of these best books, their authors, publishers and an unbiased review of books on “Estimation Theory” as well as links to the Amazon website to directly purchase these books. As an Amazon Associate, we earn from qualifying purchases, but this does not impact our reviews, comparisons, and listing of these top books; the table serves as a ready reckoner list of these best books.
List of Estimation Theory Books with author’s names, publishers, and an unbiased review as well as links to the Amazon website to directly purchase these books.
1. Theory of Estimation
1. “Statistical Decision Theory” by J Berger
“Statistical Decision Theory” Book Review: This book offers an excellent overview of the key theories and outlook of the topic. This book offers a set of mathematical techniques and quality principles, together with a collection of various statistical procedures. This book is useful in establishing the optimality as it is guided by theorists. The analysis of this book shows how this one decision principle can be applied in various practical situations. This book basically discusses more theoretical ideas and techniques of decision theory and that towards solving statistical problems. This book discusses not only what but why and where to apply the concept. This book covers topics like Utility and Loss, Prior Information and Subjective Probability, Bayesian Analysis, Minimax Analysis, Invariance, Complete and Essentially Complete Classes. This book makes sense when only looked at from the Bayesian viewpoint.


2. “Mathematical Statistics : A Decision Theoretic Approach” by T S Ferguson
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“Mathematical Statistics : A Decision Theoretic Approach” Book Review: This book covers all the basic concepts along with prerequisites. The purpose is to present the fundamentals of the subject. This book has a theoretic approach which presents an investigation of the extent to which problems of mathematical statistics may be treated by decision theory approach. This book deals mainly with the theoretical aspect that could be justified from a decisiontheoretic viewpoint. This book covers topics like Game Theory and Decision Theory, the Main Theorems of Decision Theory, Distributions and Sufficient Statistics, Invariant Statistical Decision Problems, Testing Hypotheses, Multiple Decision Problems, Sequential Decision Problems. This book is valuable for first year graduate students in mathematics.


3. “Item Response Theory: Parameter Estimation Techniques” by Frank B Baker and SeockHo Kim
“Item Response Theory: Parameter Estimation Techniques” Book Review: This book is well described and simple, helping the reader to boost his or her confidence. This book offers theory covering both the basics of item response theory and the use of R for preparing graphical presentations in writings about the theory. This book provides the reader the basic concepts of item response theory free of the tedious underlying calculations. This book is for those who possess limited knowledge of educational measurement and psychometrics. This book is clearly written and is concise and presents the concepts in a simple way and covers topics like the item Characteristics curve, Estimating the parameters of an item characteristics curve, Maximum likelihood Estimation of a Examinee Ability, The Rasch model, Bayesian parameters estimation procedures, The graded item Response, Nominally Score Items, etc.


4. “Multivariate Density Estimation: Theory, Practice, and Visualization” by David W Scott
“Multivariate Density Estimation: Theory, Practice, and Visualization”Book Review: This book is clear and concise. The key focus is on topic explanations to make a clear view of the topic. This book includes new materials and is fully updated on the topics. This book focuses on estimation techniques and methods that can be used in the field of big data. Defining optimal nonparametric estimators. This book has enough information to clarify the topic with updated graphics visualization and has enough problems to develop a firm grip on the topic. This book is ideal for theoretical and applied statisticians, practicing engineers, as well as readers interested in the theoretical aspects of nonparametric estimation and applications. This book covers topics like 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, etc along with problems at the end of each exercise and appendices.


5. “Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory” by Steven M Kay
“Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory” Book Review: This book offers an excellent overview of the key theories and outlook of the topic. The context here is wrapped up with simple words and with a detailed understanding of the subject. This book has approaches to extracting optimal estimators and analyzing its performance with real world examples and applications. This book helps in building strong intuition and expertise in designing wellperforming algorithms that solve realworld problems. This book cooperates concepts to practice by presenting useful analytical results and implementations for design, evaluation, and testing and has step by step approach to the design of algorithms with performance evaluation, metrics, testing. This book is for engineers, scientists, and advanced students in every discipline that relies on signal processing.


6. “Lessons in Estimation Theory for Signal Processing, Communications, and Control” by Jerry M Mendel
“Lessons in Estimation Theory for Signal Processing, Communications, and Control” Book Review: This book is resourceful and is written in a studentfriendly language. This book has enough theory for all the topics to be understood. This book for many branches of engineering which have the topic as their syllabus. This book helps readers to differentiate among the varied collection of estimation, methods and algorithms. This book covers topics like Introduction, Coverage, Philosophy, and Computation, The Linear Model, LeastSquares Estimation, Large & Small Sample Properties of Estimators, Properties of LeastSquares Estimators, Best Linear Unbiased Estimation, MaximumLikelihood Estimation, Multivariate Gaussian Random Variables. It also covers advanced topics like MeanSquared Estimation of Random Parameters, Maximum a Posteriori Estimation of Random Parameters, Elements of DiscreteTime GaussMarkov Random Sequences, State Estimation in detail. Some more topics like Sufficient Statistics and Statistical Estimation of Parameters, Estimation and Applications of HigherOrder Statistics, Introduction to StateVariable Models and Methods along with appendices.
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7. “Modern Spectral Estimation: Theory and Application” by Steven M Kay  
8. “Estimation with Applications to Tracking and Navigation” by Yaakov BarShalom and X Rong Li
“Estimation with Applications to Tracking and Navigation” Book Review: This is an easy to read textbook that incorporates all the topics essential for detailed knowledge of the subject. This book gives a good coverage of the design and implementation of state estimation algorithms for tracking and navigation estimation along with Applications. This book describes the design using a balanced combination of linear systems, probability, and statistics. It overviews many mathematical techniques and offers an overview of the basic concepts along with providing detailed treatment to the subject with a focus on applying the theory in real systems. This book covers topics like Basic concepts in estimation, Linear estimation in static systems, Linear estimation in static systems, Linear Dynamic Systems with random inputs, State Estimation in Discrete‐Time Linear Dynamic Systems, Estimation for Kinematic Models, Computational Aspects of Estimation, Extensions of Discrete‐Time Linear Estimation, Continuous‐Time Linear State Estimation, State Estimation for Nonlinear Dynamic Systems, Adaptive Estimation and Maneuvering Targets, Introduction to Navigation Applications, etc. This book is for graduate engineering students and engineers working in Tracking and Navigation.


9. “Detection and Estimation Theory” by Thomas Schonhoff and Arthur Giordano
“Detection and Estimation Theory” Book Review: This book is dynamic and has an engaging style. The explanation and concepts are well presented here. This book is thoroughly updated and is streamlined to reflect the upliftment in the field. This book modernizes by focussing on discrete signal processing with continuous signal presentations included to demonstrate uniformity and consistency of the results. This book reacquaints the topic readers with these topics and introducing consistent notation throughout and does not require any knowledge of MATLAB. This book is for engineers who are practising the subject. This book covers the topics such as Review of Probability, Stochastic Processes, Signal Representations and Statistics, Single Sample Detection of Binary Hypotheses, Multiple Sample Detection of Binary Hypotheses, Detection of Signals with Random Parameters, Multiple Pulse Detection with Random Parameters, Detection of Multiple Hypotheses, Nonparametric Detection. It also covers Fundamentals of Estimation Theory, Estimation of Specific and Multiple Parameters, DistributionFree Estimation–Wiener Filters, DistributionFree Estimation–Kalman Filter, Detection and Estimation in NonGaussian Noise Systems, DirectSequence SpreadSpectrum Signals in Fading Multipath Channels, Multiuser Detection, etc along with many appendices for deep understanding.


2. Advance Estimation Theory
1. “Tool Kits in Regional Science: Theory, Models, and Estimation (Advances in Spatial Science)” by Michael Sonis and Geoffrey J D Hewings
“Tool Kits in Regional Science: Theory, Models, and Estimation (Advances in Spatial Science)” Book Review: It is basically designed for the students. This book deals with equilibrium models, nonlinear dynamics, neural modelling and innovation. The topics which are covered in this book are socioeconomic system, sociospatial dynamics and nonlinear probabilistic change. It also focuses on the principles of neural spatial interaction.


2. “Stochastic Processes, Estimation, and Control (Advances in Design and Control)” by Jason L Speyer and Walter H Chung
“Stochastic Processes, Estimation, and Control (Advances in Design and Control)” Book Review: This book is designed for the graduate students. The topics which are covered in this book are probability theory and stochastic optimal control. Also covered in this book. It also focuses on the practical applications of dynamic programming for both discretetime and continuoustime systems.


3. “Advances in Estimation, Navigation, and Spacecraft Control” by Daniel Choukroun and Yaakov Oshman
“Advances in Estimation, Navigation, and Spacecraft Control” Book Review: This book is designed for the students of aerospace engineering, researchers and scientists. It basically focuses on estimation, navigation, and spacecraft control. It is divided into 27 chapters. And that can further be divided into three parts: estimation, navigation and spacecraft guidance, navigation and control.


4. “Robust Estimation and Failure Detection: A Concise Treatment (Advances in Industrial Control)” by Rami S Mangoubi
“Robust Estimation and Failure Detection: A Concise Treatment (Advances in Industrial Control)” Book Review: It is designed for the engineering students. This book focuses on robust estimation and failure detection. It also covers the kalman filtering and hinfinity filtering theory. It helps the students in getting the information regarding methods for failure detection. How to design failure detectors that are sensitive to failures but insensitive to model variations. It also focuses on how to design failure detectors that are sensitive to failures but insensitive to model variations.


5. “Parallel Algorithms for Linear Models: Numerical Methods and Estimation Problems (Advances in Computational Economics)” by Erricos Kontoghiorghes
“Parallel Algorithms for Linear Models: Numerical Methods and Estimation Problems (Advances in Computational Economics)” Book Review: It mainly focuses on the design, analysis of parallel algorithms for solving linear models. It is divided into two parts. The first part consists of four chapters and deals with the computational aspects for solving linear models. The second part consists of two chapters. And it concentrates on numerical methods of seemingly unrelated regression equations (SURE) and simultaneous equations models.


6. “Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB” by Guangzhu Xu and David M J Tax
“Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB” Book Review: It is designed for the engineering students and researchers.This book mainly focuses on the measurement system. It also provides information on neural networks, genetic state estimation and AI state estimation methods. It also focuses on adaboost and its implementation. It also provides the solved examples and questions with answers at the end of the chapters.


7. “Statistical Inference: Theory of Estimation” by Kumar S M
“Statistical Inference: Theory of Estimation” Book Review: This book is designed for the post graduate students. It provides the theorems and results on uniformly minimum variance unbiased estimators. This book introduces the different methods of estimation. The methods are methods of maximum likelihood, consistency, consistent asymptotic normality and best asymptotic normality. Chapters which cover pitman estimator, scale models, symmetry structure and empirical bayes. It also contains the solved examples, problems with answers at the end of every chapter. This book is designed for the students, researchers and scientists. It discusses the classical and Bayesian approaches. It contains the theorems, results and principles on uniformly minimum variance unbiased estimators. The topics which are covered in this book are unbiased estimation, bayes and minimax estimation. It also focuses on the Rao and Blackwell theorem. It also discusses the LehmannScheffe theorem.


8. “Model Based Parameter Estimation: Theory and Applications (Contributions in Mathematical and Computational Sciences)” by Hans Georg Bock and Thomas Carraro
“Model Based Parameter Estimation: Theory and Applications (Contributions in Mathematical and Computational Sciences)” Book Review: This book basically deals with the mathematical and numerical methods. It discusses the use of scientific computation to model, simulate, and optimize complex processes. The topics which are covered in this book are gauss newton method, parameter estimation and bayesian method. A bunch of solved numerical, and problems with answers are covered at the end of every chapter.


9. “Estimating and Costing in Civil Engineering: Theory and Practice Including Specifications and Valuations” by UBS Publishers & Distributors
“Estimating and Costing in Civil Engineering: Theory and Practice Including Specifications and Valuations” Book Review: This book is designed for the students of civil engineering. It covers the theoretical portions and practical applications also. It covers conversion tables, technical data and other information. It also discusses the drawing and designing formats used in civil engineering. It also focuses on the analysis of rates, specifications and how tender documentation is filed.


10. “Detection, Estimation and Modulation Theory – Part III” by Harry L Van Trees
“Detection, Estimation and Modulation Theory – Part III” Book Review: It is designed for the engineering students and researchers. The topics which are covered are radar, sonar, communications, seismology, biomedical engineering, and astronomy. A large number of solved examples,questions with answers, and summaries are also provided at the end of the chapter.


3. Statistical Estimation Theory
1. “Fundamentals of Statistical Processing, Volume I: Estimation Theory (PrenticeHall Signal Processing Series)” by Steven M Kay
“Fundamentals of Statistical Processing, Volume I: Estimation Theory (PrenticeHall Signal Processing Series)” Book Review: This book is designed of the students of biomedical engineering, radar engineering and physicists. It basically deals with the fundamentals of statistical processing. It focuses on the design and implementation of statistical signal processing algorithms. This book helps in designing and analyzing signal processing systems. It consists of solved examples, exercises and short questions at the end of the chapter.


2. “Theory of Point Estimation (Springer Texts in Statistics)” by Erich L Lehmann and George Casella
“Theory of Point Estimation (Springer Texts in Statistics)” Book Review: This book is designed of the students of biomedical engineering, researchers and scientists. The chapters which are covered in this book are bayesian and hierarchical bayesian approaches. It also discusses Lehmann testing statistical hypotheses. It basically deals with the theory of point point estimation. It consists of solved examples, exercises and short questions at the end of the chapter. This book is written in a studentfriendly style and teaches real subjects. Throughout the book, the theory is well presented, allowing students to have a deep dive on the topic. This book deals with the new developments concerning the information in equality and simultaneous and shrinkage estimation. In this book at the end of each chapter it provides bibliographic and historical material but also introductions to recent development in point estimation and other related topics. This book covers topics like Preparations, Unbiasedness, Equivariance, Average Risk Optimality, Minimaxity and Admissibility, Asymptotic Optimality etc.


3. “Statistical Theory: A Concise Introduction” by Felix Abramovich and Ya’acov Ritov
“Statistical Theory: A Concise Introduction” Book Review: This book is designed of the students of biomedical engineering, researchers and scientists. It covers the ideas and principles of major statistical concepts like parameter estimation and hypothesis testing. It consists of theorems, results and proofs of statistical theory. It basically deals with the main theoretical concepts used in statistical tools of linear regression. It also consists of solved examples, exercises and short questions at the end of the chapter.


4. “Statistical Decision Theory: Estimation, Testing, and Selection (Springer Series in Statistics)” by F Liese and Klaus J Miescke
“Statistical Decision Theory: Estimation, Testing, and Selection (Springer Series in Statistics)” Book Review: This book is designed for the students, researchers and scientists. It focuses on the decision theory and modern asymptotic decision theory. Various topics are covered in this book are Bayes theorem, Bayes statistical theory. It provides the information of lecam’s theory. It basically deals with statistical decision theory. Along with the theoretical portions it also covers the practical applications of statistical decision theory. It consists of solved examples, exercises and proofs at the end of the chapter.


5. “Mathematical and Statistical Estimation Approaches in Epidemiology” by Gerardo Chowell and James M Hayman
“Mathematical and Statistical Estimation Approaches in Epidemiology” Book Review: This book is designed for the students, researchers and scientists. It discusses the connections between models and disease. It focuses on the application of mathematical and statistical approaches. It basically deals with the mathematical and statistical estimation approaches in epidemiology. Along with the theoretical portions it also covers the practical applications of the analysis of infectious disease. It also consists of exercises, examples and short questions at the end of the chapter.


6. “Handbook of Fitting Statistical Distributions with R” by Zaven A Karian and Edward J Dudewicz
“Handbook of Fitting Statistical Distributions with R” Book Review: This book is designed for the students, researchers and scientists. It provides the information of cuttingedge applications. It explains the real world examples of water systems, insurance and inventory management, and materials science. It also focuses on the applications in agriculture and reliability estimation. It also discusses the understanding of the methodology and applications of fitting statistical distributions. It also consists of solved examples, exercises and short questions at the end of the chapter.


7. “Automotive Engines: Control, Estimation, Statistical Detection” by Alexander A Stotsky
“Automotive Engines: Control, Estimation, Statistical Detection” Book Review: This book is designed for the students of mechanical engineering, researchers and scientists. It discusses the speed control, cylinder flow estimation, engine torque and friction estimation. It explains the techniques useful for automotive engine control. The topics are input estimation, composite adaptation and threshold detection adaptation. Along with the theoretical portions it also covers the practical applications of the automotive engines.


8. “Fundamentals of Statistical Signal Processing, Volume II: Detection Theory: 002 (PrenticeHall Signal Processing Series)” by Steven M Kay
“Fundamentals of Statistical Signal Processing, Volume II: Detection Theory: 002 (PrenticeHall Signal Processing Series)” Book Review: This book is designed for the students of students of electrical engineering, researchers and scientists. It basically discusses the fundamentals of statistical processing. Chapters which are covered are neymanpearson theorem and handling irrelevant data. It also covers the bayes risk and multiple hypothesis testing. It also discusses the non gaussian noise and deterministic signals. It consists of solved examples, exercises and short questions at the end of the chapter.


9. “Statistical Decision Theory (SpringerBriefs in Statistics)” by Nicholas T Longford
“Statistical Decision Theory (SpringerBriefs in Statistics)” Book Review: This book is designed for the students, researchers and scientists. It basically deals with statistical decision theory. It discusses the theoretical portions as well as applications and value of decision theory. The topics which are covered are the bayes risk and multiple hypothesis testing. It consists of solved examples, exercises and short questions at the end of the chapter.


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