Lognormal Distributions - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Lognormal Distributions write by Taylor & Francis Group. This book was released on 2020-06-30. Lognormal Distributions available in PDF, EPUB and Kindle. This outstanding reference encompasses the most-up-to-date advances in lognormal distributions in thorough, detailed contributions by specialists in statistics, business and economics, industry, biology, ecology, geology, and meteorology.
Handbook of Tables for Order Statistics from Lognormal Distributions with Applications
Handbook of Tables for Order Statistics from Lognormal Distributions with Applications - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Handbook of Tables for Order Statistics from Lognormal Distributions with Applications write by N Balakrishnan. This book was released on 1999-03-31. Handbook of Tables for Order Statistics from Lognormal Distributions with Applications available in PDF, EPUB and Kindle. Lognormal distributions are one of the most commonly studied models in the sta tistical literature while being most frequently used in the applied literature. The lognormal distributions have been used in problems arising from such diverse fields as hydrology, biology, communication engineering, environmental science, reliability, agriculture, medical science, mechanical engineering, material science, and pharma cology. Though the lognormal distributions have been around from the beginning of this century (see Chapter 1), much of the work concerning inferential methods for the parameters of lognormal distributions has been done in the recent past. Most of these methods of inference, particUlarly those based on censored samples, involve extensive use of numerical methods to solve some nonlinear equations. Order statistics and their moments have been discussed quite extensively in the literature for many distributions. It is very well known that the moments of order statistics can be derived explicitly only in the case of a few distributions such as exponential, uniform, power function, Pareto, and logistic. In most other cases in cluding the lognormal case, they have to be numerically determined. The moments of order statistics from a specific lognormal distribution have been tabulated ear lier. However, the moments of order statistics from general lognormal distributions have not been discussed in the statistical literature until now primarily due to the extreme computational complexity in their numerical determination.
Lognormal Distributions
Lognormal Distributions - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Lognormal Distributions write by Crow. This book was released on 1987-12-29. Lognormal Distributions available in PDF, EPUB and Kindle. This outstanding reference encompasses the most-up-to-date advances in lognormal distributions in thorough, detailed contributions by specialists in statistics, business and economics, industry, biology, ecology, geology, and meteorology.
Entropy-Based Parameter Estimation in Hydrology
Entropy-Based Parameter Estimation in Hydrology - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Entropy-Based Parameter Estimation in Hydrology write by V.P. Singh. This book was released on 2013-04-17. Entropy-Based Parameter Estimation in Hydrology available in PDF, EPUB and Kindle. Since the pioneering work of Shannon in the late 1940's on the development of the theory of entropy and the landmark contributions of Jaynes a decade later leading to the development of the principle of maximum entropy (POME), the concept of entropy has been increasingly applied in a wide spectrum of areas, including chemistry, electronics and communications engineering, data acquisition and storage and retreival, data monitoring network design, ecology, economics, environmental engineering, earth sciences, fluid mechanics, genetics, geology, geomorphology, geophysics, geotechnical engineering, hydraulics, hydrology, image processing, management sciences, operations research, pattern recognition and identification, photogrammetry, psychology, physics and quantum mechanics, reliability analysis, reservoir engineering, statistical mechanics, thermodynamics, topology, transportation engineering, turbulence modeling, and so on. New areas finding application of entropy have since continued to unfold. The entropy concept is indeed versatile and its applicability widespread. In the area of hydrology and water resources, a range of applications of entropy have been reported during the past three decades or so. This book focuses on parameter estimation using entropy for a number of distributions frequently used in hydrology. In the entropy-based parameter estimation the distribution parameters are expressed in terms of the given information, called constraints. Thus, the method lends itself to a physical interpretation of the parameters. Because the information to be specified usually constitutes sufficient statistics for the distribution under consideration, the entropy method provides a quantitative way to express the information contained in the distribution.
Lectures on Probability Theory and Mathematical Statistics - 3rd Edition
Lectures on Probability Theory and Mathematical Statistics - 3rd Edition - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Lectures on Probability Theory and Mathematical Statistics - 3rd Edition write by Marco Taboga. This book was released on 2017-12-08. Lectures on Probability Theory and Mathematical Statistics - 3rd Edition available in PDF, EPUB and Kindle. The book is a collection of 80 short and self-contained lectures covering most of the topics that are usually taught in intermediate courses in probability theory and mathematical statistics. There are hundreds of examples, solved exercises and detailed derivations of important results. The step-by-step approach makes the book easy to understand and ideal for self-study. One of the main aims of the book is to be a time saver: it contains several results and proofs, especially on probability distributions, that are hard to find in standard references and are scattered here and there in more specialistic books. The topics covered by the book are as follows. PART 1 - MATHEMATICAL TOOLS: set theory, permutations, combinations, partitions, sequences and limits, review of differentiation and integration rules, the Gamma and Beta functions. PART 2 - FUNDAMENTALS OF PROBABILITY: events, probability, independence, conditional probability, Bayes' rule, random variables and random vectors, expected value, variance, covariance, correlation, covariance matrix, conditional distributions and conditional expectation, independent variables, indicator functions. PART 3 - ADDITIONAL TOPICS IN PROBABILITY THEORY: probabilistic inequalities, construction of probability distributions, transformations of probability distributions, moments and cross-moments, moment generating functions, characteristic functions. PART 4 - PROBABILITY DISTRIBUTIONS: Bernoulli, binomial, Poisson, uniform, exponential, normal, Chi-square, Gamma, Student's t, F, multinomial, multivariate normal, multivariate Student's t, Wishart. PART 5 - MORE DETAILS ABOUT THE NORMAL DISTRIBUTION: linear combinations, quadratic forms, partitions. PART 6 - ASYMPTOTIC THEORY: sequences of random vectors and random variables, pointwise convergence, almost sure convergence, convergence in probability, mean-square convergence, convergence in distribution, relations between modes of convergence, Laws of Large Numbers, Central Limit Theorems, Continuous Mapping Theorem, Slutsky's Theorem. PART 7 - FUNDAMENTALS OF STATISTICS: statistical inference, point estimation, set estimation, hypothesis testing, statistical inferences about the mean, statistical inferences about the variance.
