Moving Average and Autoregressive Processes. Introduction to Fourier Analysis. Spectral Theory and Filtering. Some Large Sample Theory. Estimation of the Mean and Autocorrelations. The Periodogram,… Expand

Part One. Multivariate distributions. Preliminary data analysis. Part Two: Finding new underlying variables. Principal component analysis. Factor analysis. Part Three: Procedures based on the… Expand

This paper takes a broad, pragmatic view of statistical inference to include all aspects of model formulation. The estimation of model parameters traditionally assumes that a model has a prespecified… Expand

The Dirichlet model describes how frequently-bought branded consumer products like instant coffee or toothpaste are purchased when the market is stationary and unsegmented. This is the common… Expand

Simple descriptive techniques probability models for time series estimation in the time domain forecasting stationary processes in the frequency domain spectral analysis bivariate processes linear systems state-space models and the Kalman filter non-linear models multivariate time series modelling some other topics.Expand

Probability and Statistics in Engineering and Management Science. By William W. Hines and Douglas C. Montgomery. New York, The Ronald Press Co., 1972. xii, 509 p. 914″. $13.50.

The purpose of the M2-Competition is to determine the post sample accuracy of various forecasting methods. It is an empirical study organized in such a way as to avoid the major criticism of the… Expand