Helsel D.R. Statistics for Censored Environmental Data Using Minitab and R

Second Edition. — Wiley, 2012. — 344 p. — ISBN: 0470479884.Statistical Methods for Censored Environmental Data Using Minitab and R, Second Edition introduces and explains methods for analyzing and interpreting censored data in the environmental sciences. Adapting survival analysis techniques from other fields, the book translates well-established methods from other disciplines into new solutions for environmental studies.
This new edition applies methods of survival analysis, including methods for interval-censored data to the interpretation of low-level contaminants in environmental sciences and occupational health. Now incorporating the freely available R software as well as Minitab into the discussed analyses, the book features newly developed and updated material including:
A new chapter on multivariate methods for censored data
Use of interval-censored methods for treating true nondetects as lower than and separate from values between the detection and quantitation limits ("remarked data")
A section on summing data with nondetects
A newly written introduction that discusses invasive data, showing why substitution methods fail
Expanded coverage of graphical methods for censored data
The author writes in a style that focuses on applications rather than derivations, with chapters organized by key objectives such as computing intervals, comparing groups, and correlation. Examples accompany each procedure, utilizing real-world data that can be analyzed using the Minitab and R software macros available on the book's related website, and extensive references direct readers to authoritative literature from the environmental sciences.
Statistics for Censored Environmental Data Using Minitab and R, Second Edition is an excellent book for courses on environmental statistics at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for?environmental professionals, biologists, and ecologists who focus on the water sciences, air quality, and soil science.Introduction to the First Edition: An Accident Waiting To Happen
Introduction to the Second Edition: Invasive Data
Things People Do with Censored Data that Are Just Wrong
Why Not Substitute—Missing the Signals that Are Present in the Data
Why Not Substitute?—Finding Signals that Are Not There
So Why Not Substitute?
Other Common Misuses of Censored Data
Three Approaches for Censored Data
Approach : Nonparametric Methods after Censoring at the Highest Reporting Limit
Approach : Maximum Likelihood Estimation
Approach : Nonparametric Survival Analysis Methods
Application of Survival Analysis Methods to Environmental Data
Parallels to Uncensored Methods
Reporting Limits
Limits When the Standard Deviation is Considered Constant
Insider Censoring–Biasing Interpretations
Reporting the Machine Readings of all Measurements
Limits When the Standard Deviation Changes with Concentration
For Further Study
Reporting, Storing, and Using Censored Data
Reporting and Storing Censored Data
Using Interval-Censored Data
Exercises
Plotting Censored Data
Boxplots
Histograms
Empirical Distribution Function
Survival Function Plots
Probability Plot
X–Y Scatterplots
Exercises
VI. Computing Summary Statistics and Totals
Nonparametric Methods after Censoring at the Highest Reporting Limit
Maximum Likelihood Estimation
The Nonparametric Kaplan–Meier and Turnbull Methods
ROS: A Robust Imputation Method
Methods in Excel
Handling Data with High Reporting Limits
A Review of Comparison Studies
Summing Data with Censored Observations
Exercises
Computing Interval Estimates
Parametric Intervals
Nonparametric Intervals
Intervals for Censored Data by Substitution
Intervals for Censored Data by Maximum Likelihood
Intervals for the Lognormal Distribution
Intervals Using Robust Parametric Methods
Nonparametric Intervals for Censored Data
Bootstrapped Intervals
For Further Study
Exercises
What Can be Done When All Data Are Below the Reporting Limit?
Point Estimates
Probability of Exceeding the Reporting Limit
Exceedance Probability for a Standard Higher than the Reporting Limit
Hypothesis Tests Between GroupsExercises
Comparing Two Groups
Why Not Use Substitution?
Simple Nonparametric Methods After Censoring at the Highest Reporting Limit
Maximum Likelihood Estimation
Nonparametric Methods
Value of the Information in Censored Observations
Interval-Censored Score Tests: Testing Data that Include (DL to RL) Values
Paired Observations
Summary of Two-Sample Tests for Censored Data
Exercises
Comparing Three or More Groups
Substitution Does Not Work—Invasive Data
Nonparametric Methods after Censoring at the Highest Reporting Limit
Maximum Likelihood Estimation
Nonparametric Method—The Generalized Wilcoxon TestExercises
Correlation
Types of Correlation Coefficients
Nonparametric Methods after Censoring at the Highest Reporting Limit
Maximum Likelihood Correlation Coefficient
Nonparametric Correlation Coefficient—Kendall’s Tau
Interval-Censored Score Tests: Testing Correlation with (DL to RL) Values
Summary: A Comparison Among Methods
For Further Study
Exercises
Regression and Trends
Why Not Substitute?
Nonparametric Methods After Censoring at the Highest Reporting Limit
Maximum Likelihood Estimation
Akritas–Theil–Sen Nonparametric Regression
Additional Methods for Censored Regression
Exercises
Multivariate Methods for Censored Data
A Brief Overview of Multivariate Procedures
Nonparametric Methods After Censoring at the Highest Reporting Limit
Multivariate Methods for Data with Multiple Reporting Limits
Summary of Multivariate Methods for Censored Data
The NADA for R Software
A Brief Overview of R and the NADA Software
Summary of the Commands Available in NADA
Appendix: Datasets