Global Goodness-of-Fit Tests in Logistic Regression with Sparse Data
Global Goodness-of-Fit Tests in Logistic Regression with Sparse Data
Global Goodness-of-Fit Tests in Logistic Regression with Sparse Data
You also want an ePaper? Increase the reach of your titles
YUMPU automatically turns print PDFs into web optimized ePapers that Google loves.
<strong>Data</strong>:Response1 01 Y 1 m 1 -Y 1 m 1Covariate 2 Y 2 m 2 -Y 2 m 2Pattern : : : :N Y N m N -Y N m NExample:Cont<strong>in</strong>uous covariate(s): N=M (m i ≡1)Response1 01 1 0 1Covariate 2 0 1 1Pattern : : : :N 1 0 1Model equation:πlogi = 1−πi <strong>with</strong> β j = (β 0 ,..., β p ) vector <strong>of</strong> regression parameters.Estimate parameters β j via ML.pj=0xijβjO.Kuss, <strong>Global</strong> <strong>Goodness</strong>-<strong>of</strong>-<strong>Fit</strong> <strong>Tests</strong> <strong>in</strong> <strong>Logistic</strong> <strong>Regression</strong> <strong>with</strong> <strong>Sparse</strong> <strong>Data</strong>, 2.11.02
Change language