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This essentially self-contained, deliberately compact, and user-friendly textbook is designed for a first, one-semester course in statistical signal analysis for a broad audience of students in engineering and the physical sciences. The emphasis throughout is on fundamental concepts and relationships in the statistical theory of stationary random signals, explained in a concise, yet fairly rigorous presentation. Topics and Features: · Fourier series and transforms—fundamentally important in random signal analysis and processing—are developed from scratch, emphasizing the time-domain vs. frequency-domain duality; · Basic concepts of probability theory, laws of large numbers, the central limit theorem, and statistical parametric inference procedures are presented so that no prior knowledge of probability and statistics is required; the only prerequisite is a basic two–three semester calculus sequence; · Computer simulation algorithms of stationary random signals with a given power spectrum density; · Complementary bibliography for readers who wish to pursue the study of random signals in greater depth; · Many diverse examples and end-of-chapter problems and exercises. Developed by the author over the course of many years of classroom use, A First Course in Statistics for Signal Analysis, Second Edition may be used by junior/senior undergraduates or graduate students in electrical, systems, computer, and biomedical engineering, as well as the physical sciences. The work is also an excellent resource of educational and training material for scientists and engineers working in research laboratories. This third edition contains two additional chapters that present wavelets and the uncertainty principle, and the forecasting problems for stationary time series. These two topics are essential for students to attain a deeper understanding of statistical analysis of random signals. Reviews from previous editions: A First Course in Statistics for Signal Analysis is a small, dense, and inexpensive book that covers exactly what the title says: statistics for signal analysis. The book has much to recommend it. The author clearly understands the topics presented. The topics are covered in a rigorous manner, but not so rigorous as to be ostentatious. JASA (Review of the First Edition) This is a nicely written self-contained book and it is a good candidate for adoption as a textbook for upper-level undergraduate and even for a graduate course for engineering and physical sciences students. … I have no hesitation in recommending it as a textbook for the targeted course and audience. Technometrics, Vol. 53 (4), November, 2011 (Review of the Second Edition).

Statistics . --- Fourier analysis. --- Signal processing. --- Image processing. --- Speech processing systems. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Fourier Analysis. --- Signal, Image and Speech Processing.

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This book explores different statistical quality technologies including recent advances and applications. Statistical process control, acceptance sample plans and reliability assessment are some of the essential statistical techniques in quality technologies to ensure high quality products and to reduce consumer and producer risks. Numerous statistical techniques and methodologies for quality control and improvement have been developed in recent years to help resolve current product quality issues in today’s fast changing environment. Featuring contributions from top experts in the field, this book covers three major topics: statistical process control, acceptance sampling plans, and reliability testing and designs. The topics covered in the book are timely and have a high potential impact and influence to academics, scholars, students and professionals in statistics, engineering, manufacturing and health.

Mathematical statistics. --- Statistics. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Statistics for Business, Management, Economics, Finance, Insurance. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Statistics .

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The purpose of this book is to introduce undergraduate students of engineering and the physical sciences to applied mathematics often essential to the successful solutions of practical problems. The topics selected are a review of Differential Equations, Laplace Transforms, Matrices and Determinants, Vector Analysis, Partial Differential Equations, Complex Variables, and Numerical Methods. The style of presentation is such that the step-by-step derivations may be followed by the reader with minimum assistance. Liberal use of approximately 160 examples and 1000 homework problems serves to aid students in their study. This book presents mathematical topics using derivations (similar to the technique used in engineering textbooks) rather than theorems and proofs typically found in textbooks written by mathematicians. Engineering Analysis is uniquely qualified to help apply mathematics to physical applications (spring-mass systems, electrical circuits, conduction, diffusion, etc.), in a manner as efficient and understandable as possible. This book was written to provide for an additional mathematics course after differential equations, to permit several topics to be introduced in one semester, and to make the material comprehensible to undergraduates.The book comes with an Instructor Solutions Manual, available on request, that provides solutions to all problems and also a Student Solutions Manual that provides solutions to select problems (the answers to which are given at the back of the book).

Engineering. --- Mathematical physics. --- Statistics. --- Engineering Mathematics. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Mathematical Applications in the Physical Sciences. --- Engineering mathematics. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Engineering --- Engineering analysis --- Mathematical analysis --- Mathematical analysis. --- 517.1 Mathematical analysis --- Statistics . --- Physical mathematics --- Physics

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This book presents an introduction to linear univariate and multivariate time series analysis, providing brief theoretical insights into each topic, and from the beginning illustrating the theory with software examples. As such, it quickly introduces readers to the peculiarities of each subject from both theoretical and the practical points of view. It also includes numerous examples and real-world applications that demonstrate how to handle different types of time series data. The associated software package, SSMMATLAB, is written in MATLAB and also runs on the free OCTAVE platform. The book focuses on linear time series models using a state space approach, with the Kalman filter and smoother as the main tools for model estimation, prediction and signal extraction. A chapter on state space models describes these tools and provides examples of their use with general state space models. Other topics discussed in the book include ARIMA; and transfer function and structural models; as well as signal extraction using the canonical decomposition in the univariate case, and VAR, VARMA, cointegrated VARMA, VARX, VARMAX, and multivariate structural models in the multivariate case. It also addresses spectral analysis, the use of fixed filters in a model-based approach, and automatic model identification procedures for ARIMA and transfer function models in the presence of outliers, interventions, complex seasonal patterns and other effects like Easter, trading day, etc. This book is intended for both students and researchers in various fields dealing with time series. The software provides numerous automatic procedures to handle common practical situations, but at the same time, readers with programming skills can write their own programs to deal with specific problems. Although the theoretical introduction to each topic is kept to a minimum, readers can consult the companion book ‘Multivariate Time Series With Linear State Space Structure’, by the same author, if they require more details. .

Mathematical statistics. --- Econometrics. --- Statistics. --- Computer software. --- Statistics and Computing/Statistics Programs. --- Statistics for Social Sciences, Humanities, Law. --- Mathematical Software. --- Statistics for Business, Management, Economics, Finance, Insurance. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Software, Computer --- Computer systems --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Economics, Mathematical --- Statistics .