This comprehensive and accessible textbook introduces students to the basics of modern signal processing techniques.

This book has a dual purpose. One of these is to present material which selec tively will be appropriate for a quarter or semester course in time series analysis and which will cover both the finite parameter and spectral approach. The second object is the presentation of topics of current research interest and some open questions. I mention these now. In particular, there is a discussion in Chapter III of the types of limit theorems that will imply asymptotic nor mality for covariance estimates and smoothings of the periodogram. This dis cussion allows one to get results on the asymptotic distribution of finite para meter estimates that are broader than those usually given in the literature in Chapter IV. A derivation of the asymptotic distribution for spectral (second order) estimates is given under an assumption of strong mixing in Chapter V. A discussion of higher order cumulant spectra and their large sample properties under appropriate moment conditions follows in Chapter VI. Probability density, conditional probability density and regression estimates are considered in Chapter VII under conditions of short range dependence. Chapter VIII deals with a number of topics. At first estimates for the structure function of a large class of non-Gaussian linear processes are constructed. One can determine much more about this structure or transfer function in the non-Gaussian case than one can for Gaussian processes. In particular, one can determine almost all the phase information.

Principles of Econometrics, Fifth Edition, is an introductory book for undergraduate students in economics and finance, as well as first-year graduate students in a variety of fields that include economics, finance, accounting, marketing, public policy, sociology, law, and political science. Students will gain a working knowledge of basic econometrics so they can apply modeling, estimation, inference, and forecasting techniques when working with real-world economic problems. Readers will also gain an understanding of econometrics that allows them to critically evaluate the results of others’ economic research and modeling, and that will serve as a foundation for further study of the field. This new edition of the highly-regarded econometrics text includes major revisions that both reorganize the content and present students with plentiful opportunities to practice what they have read in the form of chapter-end exercises.

Mathematical programming has know a spectacular diversification in the last few decades. This process has happened both at the level of mathematical research and at the level of the applications generated by the solution methods that were created. To write a monograph dedicated to a certain domain of mathematical programming is, under such circumstances,especially difficult. In the present monograph we opt for the domain of fractional programming. Interest of this subject was generated by the fact that various optimization problems from engineering and economics consider the minimization of a ratio between physical and/or economical functions, for example cost/time, cost/volume,cost/profit, or other quantities that measure the efficiency of a system. For example, the productivity of industrial systems, defined as the ratio between the realized services in a system within a given period of time and the utilized resources, is used as one of the best indicators of the quality of their operation. Such problems, where the objective function appears as a ratio of functions, constitute fractional programming problem. Due to its importance in modeling various decision processes in management science, operational research, and economics, and also due to its frequent appearance in other problems that are not necessarily economical, such as information theory, numerical analysis, stochastic programming, decomposition algorithms for large linear systems, etc., the fractional programming method has received particular attention in the last three decades.

This book is meant to be a quick refresher for JEE (MAIN)/AIEEE aspirants. With the aim and scope of providing a comprehensive study package for aspirants of JEE (MAIN)/AIEEE, this crash course focuses less on theory and more on concepts, formulae and tips. This is supported by plenty of practice problems based on the latest formats, structure and syllabus of JEE (MAIN)/AIEEE. This is further supplemented by a CD given along with this study kit with fully solved 2012 JEE (MAIN)/AIEEE question paper.Salient features: A Based on the latest pattern and syllabus of JEE (MAIN)/AIEEE A Solved examples, practice problems in each chapter A Previous years question papers fully solved A Less theory and more concepts, formulae and tips A Practice CD with fully solved JEE (MAIN)/AIEEE 2012 question paper A Plenty of problems for practice A Comprehensive, holistic revision of the complete syllabus of JEE (MAIN)/AIEEE A In-depth analysis of the recent trends of JEE (MAIN)/AIEEE A A quick and efficient study kit for JEE (MAIN)/AIEEE aspirants A Facilitates self-study. A Low priced, handy book for quick and efficient revision

Godfrey Beddard is Professor of Chemical Physics in the School of Chemistry, University of Leeds, where his research interests encompass femtosecond spectroscopy, electron and energy transfer, and protein folding and unfolding. 1. Numbers, Basic Functions, and Algorithms 2. Complex Numbers 3. Differentiation 4. Integration 5. Vectors 6. Matrices and Determinants 7. Matrices in Quantum Mechanics 8. Summations, Series, and Expansion of Functions 9. Fourier Series and Transforms 10. Differential Equations 11. Numerical Methods 12. Monte-carlo Methods 13. Statistics and Data Analysis

Provides a Solid Foundation for Statistical Modeling and Inference and Demonstrates Its Breadth of Applicability Stochastic Modeling and Mathematical Statistics: A Text for Statisticians and Quantitative Scientists addresses core issues in post-calculus probability and statistics in a way that is useful for statistics and mathematics majors as well