## Advanced Linear Modeling: Multivariate, Time Series, and Spatial Data; Nonparametric Regression and Response Surface MaximizationThis book introduces several topics related to linear model theory: multivariate linear models, discriminant analysis, principal components, factor analysis, time series in both the frequency and time domains, and spatial data analysis. The second edition adds new material on nonparametric regression, response surface maximization, and longitudinal models. The book provides a unified approach to these disparate subject and serves as a self-contained companion volume to the author's Plane Answers to Complex Questions: The Theory of Linear Models. Ronald Christensen is Professor of Statistics at the University of New Mexico. He is well known for his work on the theory and application of linear models having linear structure. He is the author of numerous technical articles and several books and he is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics. Also Available: Christensen, Ronald. Plane Answers to Complex Questions: The Theory of Linear Models, Second Edition (1996). New York: Springer-Verlag New York, Inc. Christensen, Ronald. Log-Linear Models and Logistic Regression, Second Edition (1997). New York: Springer-Verlag New York, Inc. |

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### Contents

Multivariate Linear Models | 1 |

Discrimination and Allocation | 74 |

Principal Components and Factor Analysis | 112 |

Frequency Analysis of Time Series | 152 |

Time Domain Analysis | 200 |

Kriging | 269 |

Nonparametric Regression | 312 |

Response Surface Maximization | 344 |

377 | |

390 | |

### Other editions - View all

Advanced Linear Modeling: Multivariate, Time Series, and Spatial Data ... Ronald Christensen No preview available - 2010 |

### Common terms and phrases

allocation rule analysis of variance approximation assume assumption autoregressive best linear predictor center points Chapter Christensen 1996a coefficients columns computed consider cosines covariance function covariance matrix defined degrees of freedom dependent variable discussed drug effects eigenvalues eigenvectors equation equivalent Example Exercise F statistic factor analysis FIGURE frequencies geostatistics given gives Haar wavelets hypothesis Kalman filter kriging lack of fit least squares estimates likelihood function linear combinations linear discrimination coordinates Mahalanobis distance maximize maximum likelihood estimates measurement error method MLEs multivariate linear model multivariate normal multivariate normal distribution nonnegative definite nonparametric regression Note observations obtain one-way ANOVA orthogonal parameters partial correlation particular periodogram placebo polynomial population principal components problem procedure Proposition random variables recursive reduced model response function sample second-order stationary process Section semivariogram spectral density standard stationary process sums of squares Table test statistic tion transformation values vector zero