Local logistic regression: An application to army penetration data

Journal of Statistical Computation and Simulation

Volumn 66, Issue 1, 2000, Pages 35-50

  Nottingham, Quinton J ^a   Birch, Jeffrey B ^a   Bodt, Barry A ^b  

^a VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

^b U S ARMY RESEARCH LABORATORY   (United States)

Author keywords

Local smoothing; Logistic regression; Non monotonic; Nonparametric

Indexed keywords

EID: 0034348473     PISSN: 00949655     EISSN: None     Source Type: Journal    
DOI: 10.1080/00949650008812010     Document Type: Article

References (19)

1. Allen, D. A. (1974) The relationship between variable selection and data augmentation and a method of prediction. Technometrics, 16, 125-127.
2. Chang, A. L. and Bodt, B. A. (1997) JTCG/AS Interlaboratory Ballistic Test Program-Final Report, U.S. Army Research Laboratory Technical Report, ARL-TR-577.
3. Chu, C.-K. and Marron, J. S. (1991) Choosing a kernel regression estimator. Statistical Science, 6(4), 404-436.
4. Cleveland, W. S. (1979) Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74, 368, 829-836.
5. Copas, J. B. (1983) Plotting p against x. Applied Statistics, 32(1), 25-31.
6. Dixon, W. J. and Mood, H. M. (1948) A Method for Obtaining and Analyzing Sensitivity Data. Journal of the American Statistical Association, 23, 109-126.
7. Einsporn, R. L. and Birch, J. B. (1993) Model-robust regression: Using nonparametric regression to improve parametric regression analyses. Technical Report Number 93-5, Virginia Polytechnic Institute and State University, Blacksburg, Virginia.
8. Fan, J. and Gijbels, I. (1992) Variable bandwidth and local linear regression smoothers. The Annals of Statistics, 20, 2008-2036.
9. Fan, J. and Gijbels, I. (1996) Local Polynomial Modeling and its Applications. London: Chapman and Hall.
10. Fan, J., Heckman, N. E. and Wand, M. P. (1995) Local polynomial kernel regression for generalized linear models and quasi-likelihood functions. Journal of the American Statistical Association, 90, 429, 141-150.
11. Härdle, W. (1990) Applied Nonparametric Regression. Cambridge University Press, New York.
12. Mays, James (1995) Model-robust regression combining parametric, nonparametric, and semiparametric methods. Unpublished Ph.D. Dissertation, Virginia Polytechnic Institute and State University, Blacksburg, Virginia.
13. McCullagh, P. and Nelder, J. A. (1983) Generalized Linear Models. New York: Chapman and Hall.
14. Myers, R. H. (1990) Classical and Modern Regression with Applications (2nd edition), PWS Kent, Boston, Massachusetts.
15. Nadaraya, E. A. (1964) On estimating regression. Theory of Probability and its Applications, 9, 141-142.
16. Nottingham, Q. J. and Birch, J. B., A semi-parametric approach to analyzing doseresponse data. Statistics in Medicine, To appear in 2000.
17. Robbins, H. and Monro, S. (1951) A Stochastic Approximation Method. Annals of Mathematical Statistics, 22, 400-407.
18. Ryan, T. P. (1997) Modern Regression Methods. New York: John Wiley and Sons.
19. Wahba, G. and Wold, S. (1975) A completely automatic French curve: fitting spline functions by cross-validation. Communications in Statistics, Series A, 4, 1-17.