2 edition of **Computer methods in linear and quadratic models.** found in the catalog.

Computer methods in linear and quadratic models.

JГёrgen KjГ¦r

- 172 Want to read
- 14 Currently reading

Published
**1970**
by Haldor Topsøe in København
.

Written in English

- Algebra -- Data processing.,
- Numerical analysis -- Data processing.

**Edition Notes**

Bibliography: p. 162.

Classifications | |
---|---|

LC Classifications | QA154 .K45 |

The Physical Object | |

Pagination | 163 p. |

Number of Pages | 163 |

ID Numbers | |

Open Library | OL5029889M |

LC Control Number | 73858179 |

A common set of mathematical tools underlies dynamic optimization, dynamic estimation, and filtering. In Recursive Models of Dynamic Linear Economies, Lars Peter Hansen and Thomas Sargent use these tools to create a class of econometrically tractable models of prices and present examples from microeconomics, macroeconomics, and asset pricing. The purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science.

testing for linear statistical models and shows their application for un-balanced data (i.e., unequal-subclass-numbers data) to certain specific models that often arise in research and survey work. In addition, three chapters are devoted to methods and results for estimating variance components, particularly from unbalanced data. Although mainly focused on classical linear models, the next several chapters also explore recent techniques for solving well-known problems that pertain to the distribution and independence of quadratic forms, the analysis of estimable linear functions and contrasts, and the general treatment of balanced random and mixed-effects models.

Read this book on Questia. Economists are increasingly using computer simulations to understand the implications of their theoretical models and to make policy recommendations. This textbook offers theoretical, algorithmic and computational guidelines for solving the most frequently encountered linear-quadratic optimization problems. It provides an overview of recent advances in control and systems theory, numerical line algebra, numerical .

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