(1976). Survival Distributions: Reliability Applications in the Biomedical Sciences. Technometrics: Vol. 18, No. 4, pp. 501-501.

Survival Distributions: Reliability Applications in the Biomedical Sciences

1 This study examined the changing status of the marine ecosystem at the island of South Georgia (Southern Ocean) using up to 27 variables measured over 22 years from three upper trophic level predators that specialize in foraging upon krill (Eupuasia superba Dana). These variables included population size, breeding performance, offspring growth rate, foraging behaviour and diet. A method was developed for reducing these multivariate time-series to a single vector, called a combined standardized index (CSI).
2 Sensitivity analyses showed that missing values had a large effect upon the accuracy of the CSI but this effect was reduced if the individual variables were highly correlated. The level of correlation and proportion of missing values within the empirical data set were within the acceptable range. Individual variables had widely varying influence upon the CSI but, in general, those with longer time-series had the greatest influence.
3 Principal components analysis showed that variables representing offspring growth tended to explain the greatest proportion of the variability in the CSI and this was followed by variables representing diet. 
4 There were 3 years in which the CSI showed extreme and significantly low values. There was a significant non-linear functional response (similar to the Holling Type II functional response) between the overall CSI and krill biomass and a similar relationship existed when the CSI was calculated for each species individually.
5 Separate analysis of variables that were likely to be representative of changing population size showed the presence of a significant decline between 1977 and 1998. There was no trend in the CSI from variables representative of foraging conditions during the summer breeding season. The study has shown that the marine ecosystem at South Georgia shows acute but transient variability that is amplified in the response of upper trophic-level predators. There is less certainty that trends in populations are a consequence of shifts in the degree to which the ecosystem can support krill-feeding seals and penguins.

Monitoring a marine ecosystem using responses of upper trophic level predators

Interval censoring appears when the event of interest is only known to have occurred within a random time interval. Estimation and hypothesis testing procedures for interval-censored data are surveyed. We distinguish between frequentist and Bayesian approaches. Computational aspects for every proposed method are described and solutions with S-Plus, whenever are feasible, are mentioned. Three real data sets are analyzed.

Frequentist and Bayesian approaches for interval-censored data

Intelligent data analysis techniques are useful for better exploring real-world data sets. However, the real-world data sets always are accompanied by missing data that is one major factor affecting data quality. At the same time, good intelligent data exploration requires quality data. Fortunately, Missing Data Imputation Techniques (MDITs) can be used to improve data quality. However, no one method MDIT can be used in all conditions, each method has its own context. In this paper, we introduce the MDITs to the KDD and machine learning communities by presenting the basic idea and highlighting the advantages and limitations of each method.

Missing Data Imputation Techniques

Imputation techniques in regression analysis: looking closely at their implementation

This paper discusses shrinkage estimation in nonparametric Bayesian survival analysis using censored data. The shrinkage estimators proposed are based on estimating the parameter measure of a prior Dirichlet process in a nonparametric Bayesian survival curve estimator which is the posterior mean of this process. The shrinkage is toward a prior family of exponential survival curves. The estimators are then compared by simulation with the wholly nonparametric estimator of Kaplan-Meier and the maximum likelihood estimator for the exponential family. These comparisons are done in cases where the exponential assumption is both

John. Van Ryzin

Papers

Shrinkage estimation in nonparametric Bayesian survival analysis: a simulation study

Computation of the mean vector and dispersion matrix for incomplete multivariate data