Statistical Modeling of Multiply Censored
Data
The statistical practices of chemists are designed both to
minimize the probabilities of misidentifying a sample compound and of falsely
reporting a detectable concentration. In environmental assessment, trace
amounts of contaminants of concern are thus often reported by the laboratory
as "nondetects" or "trace", in which case the data may be
both left and interval leftcensored. The analysis of singly censored
observations has received attention in the biostatistical (e.g. in the context
of survival analysis) and in the environmental literature (see, e.g., Akritas
et al. 1994). In particular, both maximum likelihood and semiparametric
approaches to linear models have been considered in this setting (see, e.g.,
Buckley and James 1979, Schmee and Hahn 1979, Aitkin 1981, Miller and Halpern
1982, Akritas 1996). We have developed maximum likelihood and semiparametric
approaches for the setting which includes left and interval censoring and we
are in the process of evaluating and comparing these methods through a
practical example and by simulation. An Splus program and accompanying example
which we have developed to carry out maximum likelihood linear regression with
interval and left censored data is available.
Link to the Splus
program and example.
References:
Aitkin M. (1981) A note on the regression analysis of censored
data. Technometrics, 23: 161163.
Akritas MG, Ruscitti TF and Patil GP. (1994) Statistical
Analysis of Censored Environmental Data. Handbook of Statistics 12,
Environmental Statistics (GP Patil and CR Rao, editors), NorthHolland, NY.
Akritas M.G. (1996) On the use of nonparametric regression
techniques for fitting parametric regression models. Biometrics, 52:
13421362.
Buckley J and James I. (1979) Linear regression with censored
data. Biometrika, 66: 429436.
Miller R and Halpern J. (1982) Regression with censored data.
Biometrika, 69: 521531.
Schmee J and Hahn G.J. (1979) A simple method for regression
analysis with censored data. Technometrics, 21: 417432.
HOME 
GENERAL 
EVENTS 
PEOPLE 
RESEARCH 
RESOURCES 
