JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Estimation for misclassified data with ultra-high levels
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Estimation for misclassified data with ultra-high levels
Kang, Moonsu;
  PDF(new window)
 Abstract
Outcome misclassification is widespread in classification problems, but methods to account for it are rarely used. In this paper, the problem of inference with misclassified multinomial logit data with a large number of multinomial parameters is addressed. We have had a significant swell of interest in the development of novel methods to infer misclassified data. One simulation study is shown regarding how seriously misclassification issue occurs if the number of categories increase. Then, using the group lasso regression, we will show how the best model should be fitted for that kind of multinomial regression problems comprehensively.
 Keywords
Bayesian;misclassification;multiple imputation;
 Language
English
 Cited by
 References
1.
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society B (Methodological), 58, 267-288.

2.
Bross, I. (1954). Misclassi cation in 2 by 2 tables, Biometrics, 10, 478-486. crossref(new window)

3.
Tenenbein, A. (1972). A double sampling scheme for estimating from misclassi ed multinomial data with application to sampling inspection. Technometrics, 10, 187-202.

4.
Viana, M. A. G. (1994). Bayesian small-sample estimation of misclassi ed multinomial data. Biometrics, 50, 237-243. crossref(new window)

5.
Chen, T. T. (1989). A review of methods for misclassi ed categorical data in epidemiology. Statistics in Medicine, 8, 1095-1106. crossref(new window)

6.
Ekholm, A. and Palmgren, J. (1987). Correction for misclassification using doubly sampled data. Journal of Ocial Statistics, 3, 419-429.

7.
Meier, L., Geer, S. V. D. and Buhlmann, P. (2008). The group lasso for logistic regression. Journal of the Royal Statistical Society B (Statistical Methodology), 70, 53-71. crossref(new window)

8.
Songyong, S. and Heemo, K. (2014). A polychotomous regression model with tensor product splines and direct sums. Journal of the Korean Data & Information Science Society, 25, 19-26. crossref(new window)

9.
SangIn, L. (2015). A note on standardization in penalized regressions=A note on standardization in penalized regressions. Journal of the Korean Data and Information Science Society, 26, 505-516. crossref(new window)