Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If You Use a Screen Reader

This content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.

ESTIMATION IN THE EXPONENTIAL FAMILY IN THE PRESENCE OF NUISANCE PARAMETERS: COMPROMISE BETWEEN BIAS AND PRECISION

Yue-Cune Chang and Kung-Yee Liang
Statistica Sinica
Vol. 4, No. 1 (January 1994), pp. 169-185
Stable URL: http://www.jstor.org/stable/24305279
Page Count: 17
  • Read Online (Free)
  • Subscribe ($19.50)
  • Cite this Item
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
ESTIMATION IN THE EXPONENTIAL FAMILY IN THE PRESENCE OF NUISANCE PARAMETERS: COMPROMISE BETWEEN BIAS AND PRECISION
Preview not available

Abstract

The estimation of the common odds ratio in one-to-one matched case-control studies is a typical example of the trade-off between bias and precision in public health research. Liang and Zeger (1988) proposed an estimator through estimating functions. An alternative approach motivated by reducing asymptotic MSE was presented by Kalish (1990). In this paper, a finite sample approach is conducted under a more general framework. Comparisons for pair-matched case-control studies are made among these three estimators in terms of bias, MSE, coverage probability, and length of confidence interval. Extension to the multidimensional case is also presented.

Page Thumbnails

  • Thumbnail: Page 
[169]
    [169]
  • Thumbnail: Page 
170
    170
  • Thumbnail: Page 
171
    171
  • Thumbnail: Page 
172
    172
  • Thumbnail: Page 
173
    173
  • Thumbnail: Page 
174
    174
  • Thumbnail: Page 
175
    175
  • Thumbnail: Page 
176
    176
  • Thumbnail: Page 
177
    177
  • Thumbnail: Page 
178
    178
  • Thumbnail: Page 
179
    179
  • Thumbnail: Page 
180
    180
  • Thumbnail: Page 
181
    181
  • Thumbnail: Page 
182
    182
  • Thumbnail: Page 
183
    183
  • Thumbnail: Page 
184
    184
  • Thumbnail: Page 
185
    185