scholarly journals Simple Marginally Noninformative Prior Distributions for Covariance Matrices

2013 ◽  
Vol 8 (2) ◽  
pp. 439-452 ◽  
Author(s):  
Alan Huang ◽  
M. P. Wand
Keyword(s):  
Covariance Matrices ◽  
Prior Distributions ◽  
Noninformative Prior

scholarly journals An Assessment of the Effects of Prior Distributions on the Bayesian Predictive Inference

2016 ◽  
Vol 5 (5) ◽  
pp. 31
Author(s):  
Azizur Rahman ◽  
Junbin Gao ◽  
Catherine D'Este ◽  
Syed Ejaz Ahmed
Keyword(s):  
Statistical Analysis ◽  
Linear Regression ◽  
Regression Models ◽  
Prior Information ◽  
Predictive Inference ◽  
Shape Parameters ◽  
Linear Regression Models ◽  
Prior Distributions ◽  
Noninformative Prior ◽  
Observable Data

Predictive inference is one of the oldest methods of statistical analysis and it is based on observable data. Prior information plays an important role in the Bayesian methodology. Researchers in this field are often subjective to exercise noninformative prior. This study tests the effects of a range of prior distributions on the Bayesian predictive inference for different modelling situations such as linear regression models under normal and Student-t errors. Findings reveal that different choice of priors not only provide different prediction distributions of the future response(s)  but also change the location and/or scale or shape parameters of the prediction distributions.

Bayesian Estimate of Exponential Parameter with Missing Data

2013 ◽  
Vol 321-324 ◽  
pp. 904-908
Author(s):  
Cheng Dong Wei ◽  
Fu Wang ◽  
Huan Qi Wei
Keyword(s):  
Numerical Simulation ◽  
Missing Data ◽  
Bayesian Estimation ◽  
Bayesian Estimate ◽  
Exponential Parameter ◽  
Empirical Bayesian ◽  
Prior Distributions ◽  
Noninformative Prior ◽  
Simulation Results ◽  
Matlab Programming

We discuss the empirical Bayesian estimation and the noninformative prior Bayesian estimation of Exponential parameter in the missing data occasion. By setting different prior distributions, we get different bayesian risks and compare the numerical simulation results through the MATLAB programming.

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.

Pemberian Umpan Balik Dalam Meningkatkan Hasil Belajar dan Minat Belajar Mahasiswa

2015 ◽  
Vol 4 (3) ◽  
Author(s):  
Seruni Seruni ◽  
Nurul Hikmah
Keyword(s):  
Information Technology ◽  
Learning Outcomes ◽  
Test Data ◽  
Random Sampling ◽  
Covariance Matrices ◽  
Test Results ◽  
Immediate Feedback ◽  
Statistics Course ◽  
Independent Variable ◽  
Descriptive Statistical Analysis

<p>The purpose of this study is to find and analyze the effect of feedback on <br />learning outcomes in mathematics and an interest in basic statistics course. The <br />population in this study are affordable Information Technology Student cademic Year 2012/2013 Semester II Indraprasta PGRI University of South Jakarta. Sample The study sample was obtained through random sampling. This study used an experimental method to the analysis using the MANOVA test. This study has three variables, consisting of: one independent variable, namely the provision of feedback (immediate and delayed), and two dependent variable is the result of interest in the study of mathematics and basic statistics course. The data was collected for the test results to learn mathematics, and a questionnaire for the interest in basic statistics course. Collected data were analyzed using the MANOVA test. Before the data were analyzed, first performed descriptive statistical analysis and test data analysis requirements (test data normality and homogeneity of covariance matrices). The results show that the learning outcomes of interest in mathematics and basic statistics course for students who are given immediate feedback higher than students given feedback delayed. <br /><br /></p>

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