Dr Nick

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About Dr Nick

Levels Tought:
Elementary,Middle School,High School,College,University,PHD

Expertise:
Art & Design,Computer Science See all
Art & Design,Computer Science,Engineering,Information Systems,Programming Hide all
Teaching Since: May 2017
Last Sign in: 249 Weeks Ago, 2 Days Ago
Questions Answered: 19234
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Education

  • MBA (IT), PHD
    Kaplan University
    Apr-2009 - Mar-2014

Experience

  • Professor
    University of Santo Tomas
    Aug-2006 - Present

Category > Statistics Posted 10 Nov 2017 My Price 15.00

the two are governed by two different Gauss distribution the N(0,σ2M);M=I or M=Cov part Where: I= "Identity Matrix and " Cov=

It is quantitative

 

Ordinary least squares is a technique for estimating unknown parameters in a linear regression model. OLS yield the maximum likelihood in a vector β

, assuming the parameters have equal variance and are uncorrelated, in a noise ε - homoscedastic.

 

y→=Xβ→+ε→

 

Generalized least squares allows this approach to be generalized to give the maximum likelihood estimate β

when the noise is of unequal variance (heteroscedasticity). Typically this leads to mathematical treatment that presents the two as follows: OLS: Y→=Xβ+ε where ε~N(0,σ²I) GLS: Y→=Xβ+η where η~N(0,σ²Cov) Note the formulation for the two approaches results in real structural and quantitative difference. Notice the two are governed by two different Gauss distribution the N(0,σ2M);M=I or M=Cov part Where: I= "Identity Matrix and " Cov=

"Covariance Matrix

 

Cheers!

Answers

Dr Nick
(3)
Status NEW Posted 10 Nov 2017 10:11 AM My Price 15.00

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