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Stat 111 Lecture 16: Score and Wald Tests 1 Lecture Outline • Score Tests • Wald Tests • Comparison of the 3 Likelihood Tests 2 The Score Function • Let L(θ) be the likelihood, and l(θ) = log[L(θ)] be the log-likelihood. • In order to calculate the MLEs, what do we need to calculate? • The score function, U(θ), is the derivative of the log- likelihood w.r.t. θ: • We’ve been using the score functi
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ï¼»2014å¹´11月15æ—¥ã„ã‚ã生涯å¦ç¿’プラザã§ã®ãƒˆãƒ¼ã‚¯ã®æœ€å¾Œã®éƒ¨åˆ†ã‚’æ‹¡å¼µã—ãŸã‚‚ã®ã§ã™ï¼½ ç°¡å˜ãªä¾‹é¡Œ å¹³å‡10，標準åå·®3ã®æ£è¦åˆ†å¸ƒã®å¯†åº¦é–¢æ•° $\frac{1}{\sqrt{2\pi}\cdot 3} e^{-(x-10)^2/(2\cdot 3^2)}$ ã¨æŒ‡æ•°é–¢æ•° $e^{-x/10}$ ã¨ã‚’ $50:10$ ã§æ··ãœåˆã‚ã›ãŸç°¡å˜ãªé–¢æ•°ã‚’考ãˆã¾ã™ï¼š \[ \mu(x) = \frac{50}{\sqrt{2\pi}\cdot 3} e^{-(x-10)^2/(2\cdot 3^2)} + 10e^{-x/10} \] $x = 1,2,\ldots,20$ ã«ã¤ã„ã¦ï¼Œä¸Šã®å¼ã§ä¸Žãˆã‚‰ã‚Œã‚‹å€¤ã‚’å¹³å‡å€¤ã¨ã™ã‚‹ãƒã‚¢ã‚½ãƒ³åˆ†å¸ƒ $\displaystyle p_y = \frac{\mu^y e^{-\mu}}{y!}$ ã®ä¹±æ•° $y$ を発生ã—ã¾ã™ï¼š f = function(x) { 50*dno
How to test for simultaneous equality of choosen coefficients in logit or probit model?
Wald test One standard approach is the Wald test. This is what the Stata command test does after a logit or probit regression. Let's see how this works in R by looking at an example: mydata <- read.csv("http://www.ats.ucla.edu/stat/data/binary.csv") # Load dataset from the web mydata$rank <- factor(mydata$rank) mylogit <- glm(admit ~ gre + gpa + rank, data = mydata, family = "binomial") # calculat
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