Download presentation

Presentation is loading. Please wait.

Published byKaleigh Renshaw Modified about 1 year ago

1
STATISTICAL INFERENCE PART IV LOCATION AND SCALE PARAMETERS 1

2
2 LOCATION PARAMETER Let f(x) be any pdf. The family of pdfs f(x ) indexed by parameter is called the location family with standard pdf f(x) and is the location parameter for the family. Equivalently, is a location parameter for f(x) iff the distribution of X does not depend on .

3
Example If X~N(θ,1), then X-θ~N(0,1) distribution is independent of θ. θ is a location parameter. If X~N(0,θ), then X-θ~N(-θ,θ) distribution is NOT independent of θ. θ is NOT a location parameter. 3

4
4 LOCATION PARAMETER Let X 1,X 2,…,X n be a r.s. of a distribution with pdf (or pmf); f(x; ); . An estimator t(x 1,…,x n ) is defined to be a location equivariant iff t(x 1 +c,…,x n +c)= t(x 1,…,x n ) +c for all values of x 1,…,x n and a constant c. t(x 1,…,x n ) is location invariant iff t(x 1 +c,…,x n +c)= t(x 1,…,x n ) for all values of x 1,…,x n and a constant c. Invariant = does not change

5
Example Is location invariant or equivariant estimator? Let t(x 1,…,x n ) =. Then, t(x 1 +c,…,x n +c)= (x 1 +c+…+x n +c)/n = (x 1 +…+x n +nc)/n = +c = t(x 1,…,x n ) +c location equivariant 5

6
Example Is s² location invariant or equivariant estimator? Let t(x 1,…,x n ) = s²= Then, t(x 1 +c,…,x n +c)= =t(x 1,…,x n ) Location invariant 6 (x 1,…,x n ) and (x 1 +c,…,x n +c) are located at different points on real line, but spread among the sample values is same for both samples.

7
7 SCALE PARAMETER Let f(x) be any pdf. The family of pdfs f(x/ )/ for >0, indexed by parameter , is called the scale family with standard pdf f(x) and is the scale parameter for the family. Equivalently, is a scale parameter for f(x) iff the distribution of X/ does not depend on .

8
Example Let X~Exp(θ). Let Y=X/θ. You can show that f(y)=exp(-y) for y>0 Distribution is free of θ θ is scale parameter. 8

9
9 SCALE PARAMETER Let X 1,X 2,…,X n be a r.s. of a distribution with pdf (or pmf); f(x; ); . An estimator t(x 1,…,x n ) is defined to be a scale equivariant iff t(cx 1,…,cx n )= ct(x 1,…,x n ) for all values of x 1,…,x n and a constant c>0. t(x 1,…,x n ) is scale invariant iff t(cx 1,…,cx n )= t(x 1,…,x n ) for all values of x 1,…,x n and a constant c>0.

10
Example Is scale invariant or equivariant estimator? Let t(x 1,…,x n ) =. Then, t(cx 1,…,cx n )= c(x 1 +…+x n )/n = c = ct(x 1,…,x n ) Scale equivariant 10

11
11 LOATION-SCALE PARAMETER Let f(x) be any pdf. The family of pdfs f((x ) / )/ for >0, indexed by parameter ( , ), is called the location-scale family with standard pdf f(x) and is a location parameter and is the scale parameter for the family. Equivalently, is a location parameter and is a scale parameter for f(x) iff the distribution of (X )/ does not depend on and .

12
Example 1. X~N(μ,σ²). Then, Y=(X- μ)/σ ~ N(0,1) Distribution is independent of μ and σ² μ and σ² are location-scale paramaters 2. X~Cauchy(θ,β). You can show that the p.d.f. of Y=(X- β)/ θ is f(y) = 1/(π(1+y²)) β and θ are location-and-scale parameters. 12

13
13 LOCATION-SCALE PARAMETER Let X 1,X 2,…,X n be a r.s. of a distribution with pdf (or pmf); f(x; ); . An estimator t(x 1,…,x n ) is defined to be a location-scale equivariant iff t(cx 1 +d,…,cx n +d)= ct(x 1,…,x n )+d for all values of x 1,…,x n and a constant c>0. t(x 1,…,x n ) is location-scale invariant iff t(cx 1 +d,…,cx n +d)= t(x 1,…,x n ) for all values of x 1,…,x n and a constant c>0.

Similar presentations

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google