IMSL Statistics Reference Guide
PV‑WAVE IMSL Statistics is a powerful tool for mathematical, statistical, and scientific computing. This PV-WAVE IMSL Statistics Reference documents the routines that support this functionality. Each function and procedure is designed for use in research as well as in technical applications.
The topics in this guide are organized as follows:
Chapter 1: Introduction—Introduces PV-WAVE IMSL Statistics and covers some of the basic concepts found in this guide.
Chapter 3: Regression—Discusses stepwise regression, all best regression, multiple linear regression models, polynomial models and nonlinear models.
Chapter 4: Correlation and Covariance—Discusses sample variance-covariance,partial correlation and covariances, pooled variance-covariance and robust estimates of a covariance matrix and mean factor.
Chapter 9: Time Series and Forecasting—Discusses analysis and forecasting of time series using a nonseasonal ARMA model, GARCH (Generalized Autoregressive Conditional Heteroskedasticity), Kalman filtering, Automatic Model Selection, Bayesian Seasonal Analysis and Prediction, Optimum Controller Design, Spectral Density Estimation, portmanteau lack of fit test and difference of a seasonal or nonseasonal time series.
Chapter 13: Random Number Generation—Discusses the Mersenne Twister generator and a generator for multivariate normal distributions and pseudorandom numbers from several distributions, including gamma, Poisson, beta, and low discrepancy sequence.
Chapter 14: Data Mining—Discusses genetic algorithms, Naive Bayes functions, and forecasting, classification, and statistical pattern recognition using neural networks.
Chapter 15: Utilities—Discusses machine, mathematical, physical constants, retrieval of machine constants and customizable error handling.