Statistical inference for correlated data in ophthalmologic studies

2006 ◽  
Vol 25 (16) ◽  
pp. 2771-2783 ◽  
Author(s):  
Man-Lai Tang ◽  
Nian-Sheng Tang ◽  
Bernard Rosner
Keyword(s):  
Statistical Inference ◽  
Correlated Data

Essentials of Statistical Inference

2005 ◽  
Author(s):  
G. A. Young ◽  
R. L. Smith
Keyword(s):  
Statistical Inference

Randomization and Statistical Inference

1975 ◽  
Vol 4 (8) ◽  
pp. 723-735
Author(s):  
Robert Easterling
Keyword(s):  
Statistical Inference

Statistical Inference for Measurement Models

1970 ◽  
Vol 15 (6) ◽  
pp. 402, 404-405
Author(s):  
ROBERT E. DEAR
Keyword(s):  
Statistical Inference ◽  
Measurement Models

Review of Elements of statistics: An introduction to probability and statistical inference. 3rd ed.

1980 ◽  
Vol 25 (10) ◽  
pp. 851-852
Author(s):  
GEOFFREY KEPPEL
Keyword(s):  
Statistical Inference

Introduction to statistical inference.

1957 ◽  
Author(s):  
Jerome C. R. Li
Keyword(s):  
Statistical Inference

Quadratic Discriminant Analysis of Spatially Correlated Data

2001 ◽  
Vol 6 (2) ◽  
pp. 15-28 ◽  
Author(s):  
K. Dučinskas ◽  
J. Šaltytė
Keyword(s):  
Discriminant Function ◽  
Error Rate ◽  
Gaussian Random Field ◽  
Correlated Data ◽  
Covariance Matrices ◽  
Classification Rule ◽  
Spatial Correlations ◽  
Linear Quadratic ◽  
Spatial Correlation Function ◽  
Spatially Correlated

The problem of classification of the realisation of the stationary univariate Gaussian random field into one of two populations with different means and different factorised covariance matrices is considered. In such a case optimal classification rule in the sense of minimum probability of misclassification is associated with non-linear (quadratic) discriminant function. Unknown means and the covariance matrices of the feature vector components are estimated from spatially correlated training samples using the maximum likelihood approach and assuming spatial correlations to be known. Explicit formula of Bayes error rate and the first-order asymptotic expansion of the expected error rate associated with quadratic plug-in discriminant function are presented. A set of numerical calculations for the spherical spatial correlation function is performed and two different spatial sampling designs are compared.

Sparse Sensing for Statistical Inference

2016 ◽  
Author(s):  
Sundeep Prabhakar Chepuri ◽  
Geert Leus
Keyword(s):  
Statistical Inference ◽  
Sparse Sensing
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