Author: M. Khoshnevisan, S. Saxena, H. P. Singh, S. Singh, F. SmarandachePublisher: Infinite Study
ISBN: 1931233683
Category : Estimation theory
Languages : en
Pages : 63
Book Description
Randomness and Optimal Estimation in Data Sampling
Author: M. Khoshnevisan, S. Saxena, H. P. Singh, S. Singh, F. SmarandachePublisher: Infinite Study
ISBN: 1931233683
Category : Estimation theory
Languages : en
Pages : 63
Book Description
Randomness and Optimal Estimation in Data Sampling
Author: Dr. Jack AllenPublisher:
ISBN: 9781931233545
Category : Mathematics
Languages : en
Pages : 62
Book Description
Randomness and Optimal Estimation in Data Sampling
Author: Publisher:
ISBN: 9781461929826
Category : Estimation theory
Languages : en
Pages : 63
Book Description
Optimal Sampling Design and Parameter Estimation of Gaussian Random Fields
Author: Zhengyuan ZhuPublisher:
ISBN:
Category : Fractals
Languages : en
Pages : 132
Book Description
Advanced Sampling Theory with Applications
Author: S. SinghPublisher: Springer Science & Business Media
ISBN: 9400707894
Category : Medical
Languages : en
Pages : 1242
Book Description
This book is a multi-purpose document. It can be used as a text by teachers, as a reference manual by researchers, and as a practical guide by statisticians. It covers 1165 references from different research journals through almost 1900 citations across 1194 pages, a large number of complete proofs of theorems, important results such as corollaries, and 324 unsolved exercises from several research papers. It includes 159 solved, data-based, real life numerical examples in disciplines such as Agriculture, Demography, Social Science, Applied Economics, Engineering, Medicine, and Survey Sampling. These solved examples are very useful for an understanding of the applications of advanced sampling theory in our daily life and in diverse fields of science. An additional 173 unsolved practical problems are given at the end of the chapters. University and college professors may find these useful when assigning exercises to students. Each exercise gives exposure to several complete research papers for researchers/students.
Advanced Sampling Theory With Applications
Author: Sarjinder SinghPublisher: Springer Science & Business Media
ISBN: 9781402017070
Category : Mathematics
Languages : en
Pages : 640
Book Description
A comprehensive expose of basic and advanced sampling techniques along with their applications in the diverse fields of science and technology.
Frontiers in Massive Data Analysis
Author: National Research CouncilPublisher: National Academies Press
ISBN: 0309287812
Category : Mathematics
Languages : en
Pages : 191
Book Description
Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.
Optimal Linear Estimation of Bounds of Random Variables
Author: STANFORD UNIV CALIF DEPT OF STATISTICS.Publisher:
ISBN:
Category :
Languages : en
Pages : 8
Book Description
The problem of estimating the bounds of random variables has been previously discussed. Here we discuss optimality of estimates when the data is censored so that only the r largest or smallest of the observations is available for estimating a bound. For fixed r we find the linear function of the censored data which is the optimal estimator of a bound in the sense that, when the sample size is large, the estimator has smallest mean squared error among all such linear estimators. Provided r is not close to one, these estimators are almost optimal when the entire sample is available since, for example, when estimating an upper bound and the sample size is large, the largest few observations carry most of the information about the bound. This fact is illustrated in one case.
Optimal Estimation of Dynamic Systems
Author: John L. CrassidisPublisher: CRC Press
ISBN: 0203509129
Category : Mathematics
Languages : en
Pages : 606
Book Description
Most newcomers to the field of linear stochastic estimation go through a difficult process in understanding and applying the theory.This book minimizes the process while introducing the fundamentals of optimal estimation. Optimal Estimation of Dynamic Systems explores topics that are important in the field of control where the signals receiv
Author: