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The Nature of Statistical Learning Theory

by Vladimir N. Vapnik , 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
Abstract - Cited by 13236 (32 self) - Add to MetaCart
Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based

Statistical Analysis of Cointegrated Vectors

by Soren Johansen - Journal of Economic Dynamics and Control , 1988
"... We consider a nonstationary vector autoregressive process which is integrated of order 1, and generated by i.i.d. Gaussian errors. We then derive the maximum likelihood estimator of the space of cointegration vectors and the likelihood ratio test of the hypothesis that it has a given number of dimen ..."
Abstract - Cited by 2749 (12 self) - Add to MetaCart
of dimensions. Further we test linear hypotheses about the cointegration vectors. The asymptotic distribution of these test statistics are found and the first is described by a natural multivariate version of the usual test for unit root in an autoregressive process, and the other is a x2 test. 1.

On the statistical analysis of dirty pictures

by Julian Besag - JOURNAL OF THE ROYAL STATISTICAL SOCIETY B , 1986
"... ..."
Abstract - Cited by 1248 (5 self) - Add to MetaCart
Abstract not found

Statistical Analysis with Missing Data

by Roderick J. Little, Nanhua Zhang , 2002
"... Subsample ignorable likelihood for regression ..."
Abstract - Cited by 2769 (21 self) - Add to MetaCart
Subsample ignorable likelihood for regression

Statistical analysis in Climate Research

by Hans Von Storch, Francis W. Zwiers Cambridge , 1999
"... Climate is a complex system: it has many variables, and they are acting nonlinearly, in general. Therefore, no exact answers to questions should be expected, and many climatic processes are and will be poorly understood. That means that statistical analysis is undeniable in climate research. This si ..."
Abstract - Cited by 383 (9 self) - Add to MetaCart
Climate is a complex system: it has many variables, and they are acting nonlinearly, in general. Therefore, no exact answers to questions should be expected, and many climatic processes are and will be poorly understood. That means that statistical analysis is undeniable in climate research

Self-Similarity Through High-Variability: Statistical Analysis of Ethernet LAN Traffic at the Source Level

by Walter Willinger, Murad S. Taqqu, Robert Sherman, Daniel V. Wilson - IEEE/ACM TRANSACTIONS ON NETWORKING , 1997
"... A number of recent empirical studies of traffic measurements from a variety of working packet networks have convincingly demonstrated that actual network traffic is self-similar or long-range dependent in nature (i.e., bursty over a wide range of time scales) -- in sharp contrast to commonly made tr ..."
Abstract - Cited by 743 (24 self) - Add to MetaCart
traffic modeling assumptions. In this paper, we provide a plausible physical explanation for the occurrence of self-similarity in LAN traffic. Our explanation is based on new convergence results for processes that exhibit high variability (i.e., infinite variance) and is supported by detailed statistical

Export versus FDI with Heterogenous Firms

by Elhanan Helpman, Marc J. Melitz, Stephen R. Yeaple - American Economic Review , 2004
"... The statistical analysis of Þrm level data on U.S. Multinational Corporations reported in this ..."
Abstract - Cited by 658 (21 self) - Add to MetaCart
The statistical analysis of Þrm level data on U.S. Multinational Corporations reported in this

Directional Statistics and Shape Analysis

by K. V. Mardia , 1995
"... There have been various developments in shape analysis in the last decade. We describe here some relationships of shape analysis with directional statistics. For shape, rotations are to be integrated out or to be optimized over whilst they are the basis for directional statistics. However, various c ..."
Abstract - Cited by 794 (33 self) - Add to MetaCart
There have been various developments in shape analysis in the last decade. We describe here some relationships of shape analysis with directional statistics. For shape, rotations are to be integrated out or to be optimized over whilst they are the basis for directional statistics. However, various

Accurate Methods for the Statistics of Surprise and Coincidence

by Ted Dunning - COMPUTATIONAL LINGUISTICS , 1993
"... Much work has been done on the statistical analysis of text. In some cases reported in the literature, inappropriate statistical methods have been used, and statistical significance of results have not been addressed. In particular, asymptotic normality assumptions have often been used unjustifiably ..."
Abstract - Cited by 1057 (1 self) - Add to MetaCart
Much work has been done on the statistical analysis of text. In some cases reported in the literature, inappropriate statistical methods have been used, and statistical significance of results have not been addressed. In particular, asymptotic normality assumptions have often been used

Blind Signal Separation: Statistical Principles

by Jean-Francois Cardoso , 2003
"... Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis, aiming at recovering unobserved signals or `sources' from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption of mut ..."
Abstract - Cited by 529 (4 self) - Add to MetaCart
Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis, aiming at recovering unobserved signals or `sources' from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption
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