note10_regression

Posterior predictive simulation analytic form of the

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Unformatted text preview: ledge of β and σ 2 is summarized by our posterior distribution. First draw (β, σ 2 ) from ˜ their joint posterior distribution, then draw y ∼ N (Xβ, σ 2 I ). ˜ • Posterior predictive simulation: • Analytic form of the posterior predictive distribution: ˜ˆ p(˜|y ) is multivariate t with location X β , square scale matrix y 2 T −1 ˜ ˜ s (I + X (X X ) X ), and n − k degrees of freedom. & % ' $ Model checking and robustness • Suppose one simulates many samples y1 , . . . , yn from the ˜ ˜ posterior predictive distribution conditional on the same covariate vectors, x1 , . . . , xn used to simulate the data. Slide 6 • To judge if a particular response value yi is consistent with the tted model, one looks at the position of yi relative to the histogram of simulated values of yi from the corresponding ˜ predictive distribution. • If yi is in the tail of the distribution, that indicates that this observation is a potential outlier. & % MATH-440 Linear Regression ' $ Example Measurements on breeding pairs of land-bird species were collected from 16 islands around Britain over the course of several decades. The dataset birdextint.txt contains the following variables for each species: • TIME: the average time of extinction of the species on the Slide 7 island where it appeared • NESTING: the average number of nesting pairs • SIZE: the size of the species (0=small or 1=large) • STATUS: the migratory status of the species (0=migrant or 1=resident) The objective is to t a model that relates the time of extinction of the bird species to the covariates. & ' Slide 8 % $ setwd("H:/Math440") bird = read.table("birdextinct.txt", header=T, sep="\t") attach(bird) hist(TIME) The distribution of the outcome variable, TIME, is strongly right-skewed. Let's transform it to the log-scale: LOGTIME = log(TIME) hist(LOGTIME) & % MATH-440 Linear Regression ' $ 0 10 20 Slide 9 30 Frequency 40 50 Histogram of TIME 0 10 20 30 40 50 60 TIME & % ' $ 8 0 2 4 Slide 10 6 Frequency 10 12 14 Histogram of LOGTIME 0 1 2 3 4 LOGTIME & % MATH-440 Linear Regression ' $ Let us look at the relationship between LOGTIME and the three predictor variables. Slide 11 plot(NESTING, LOGTIME) out = (LOGTIME > 3) text(NESTING[out], LOGTIME[out], label=SPECIES[out], pos=2) plot(ji...
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This note was uploaded on 02/22/2013 for the course MATH 440 taught by Professor Tadesse during the Spring '13 term at Georgetown.

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