Best Reference Books – Theory of Estimation

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We have compiled the list of Top 10 Best Reference Books on Theory of Estimation subject. These books are used by students of top universities, institutes and colleges. Here is the full list of top 10 best books on Theory of Estimation along with reviews.

Kindly note that we have put a lot of effort into researching the best books on Theory of Estimation subject and came out with a recommended list of top 10 best books. The table below contains the Name of these best books, their authors, publishers and an unbiased review of books on "Theory of Estimation" 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.

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 decision-theoretic 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. “Theory of Point Estimation” by E L Lehmann

“Theory of Point Estimation” Book Review: This book is written in a student-friendly 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.

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4. “Item Response Theory: Parameter Estimation Techniques” by Frank B Baker and Seock-Ho 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.

5. “Multivariate Density Estimation: Theory, Practice, and Visualization” by David W Scott

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“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.

6. “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 well-performing algorithms that solve real-world 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.

7. “Lessons in Estimation Theory for Signal Processing, Communications, and Control” by Jerry M Mendel

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“Lessons in Estimation Theory for Signal Processing, Communications, and Control” Book Review: This book is resourceful and is written in a student-friendly 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, Least-Squares Estimation, Large & Small Sample Properties of Estimators, Properties of Least-Squares Estimators, Best Linear Unbiased Estimation, Maximum-Likelihood Estimation, Multivariate Gaussian Random Variables. It also covers advanced topics like Mean-Squared Estimation of Random Parameters, Maximum a Posteriori Estimation of Random Parameters, Elements of Discrete-Time Gauss-Markov Random Sequences, State Estimation in detail. Some more topics like Sufficient Statistics and Statistical Estimation of Parameters, Estimation and Applications of Higher-Order Statistics, Introduction to State-Variable Models and Methods along with appendices.

8. “Modern Spectral Estimation: Theory and Application” by Steven M Kay
9. “Estimation with Applications to Tracking and Navigation” by Yaakov Bar-Shalom 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.

10. “Detection and Estimation Theory” by Thomas Schonhoff and Arthur Giordano

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“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, Distribution-Free Estimation–Wiener Filters, Distribution-Free Estimation–Kalman Filter, Detection and Estimation in Non-Gaussian Noise Systems, Direct-Sequence Spread-Spectrum Signals in Fading Multipath Channels, Multiuser Detection, etc along with many appendices for deep understanding.

People who are searching for Free downloads of books and free pdf copies of these top 10 books on Theory of Estimation – we would like to mention that we don’t have free downloadable pdf copies of these good books and one should look for free pdf copies from these Authors only if they have explicitly made it free to download and read them.

We have created a collection of best reference books on "Theory of Estimation" so that one can readily see the list of top books on "Theory of Estimation" and buy the books either online or offline.

If any more book needs to be added to the list of best books on Theory of Estimation subject, please let us know.

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Manish Bhojasia, a technology veteran with 20+ years @ Cisco & Wipro, is Founder and CTO at Sanfoundry. He is Linux Kernel Developer & SAN Architect and is passionate about competency developments in these areas. He lives in Bangalore and delivers focused training sessions to IT professionals in Linux Kernel, Linux Debugging, Linux Device Drivers, Linux Networking, Linux Storage, Advanced C Programming, SAN Storage Technologies, SCSI Internals & Storage Protocols such as iSCSI & Fiber Channel. Stay connected with him @ LinkedIn | Youtube | Instagram | Facebook | Twitter