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Local Regression and Likelihood

Posted By: AvaxGenius
Date: 11 Dec 2023 16:54:58
Local Regression and Likelihood

Local Regression and Likelihood by Clive Loader
English | PDF(True) | 1999 | 297 Pages | ISBN : 0387987754 | 3.2 MB

Separation of signal from noise is the most fundamental problem in data analysis, and arises in many fields, for example, signal processing, econometrics, acturial science, and geostatistics. This book introduces the local regression method in univariate and multivariate settings, and extensions to local likelihood and density estimation. Basic theoretical results and diagnostic tools such as cross validation are introduced along the way. Examples illustrate the implementation of the methods using the LOCFIT software.
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Probability and Statistical Models: Foundations for Problems in Reliability and Financial Mathematics (Repost)

Posted By: AvaxGenius
Date: 24 Apr 2022 06:24:57
Probability and Statistical Models: Foundations for Problems in Reliability and Financial Mathematics (Repost)

Probability and Statistical Models: Foundations for Problems in Reliability and Financial Mathematics by Arjun K. Gupta
English | PDF | 2010 | 270 Pages | ISBN : 0817649867 | 1.7 MB

With an emphasis on models and techniques, this textbook introduces many of the fundamental concepts of stochastic modeling that are now a vital component of almost every scientific investigation. These models form the basis of well-known parametric lifetime distributions such as exponential, Weibull, and gamma distributions, as well as change-point and mixture models.
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New Introduction to Multiple Time Series Analysis

Posted By: AvaxGenius
Date: 11 Mar 2022 22:06:36
New Introduction to Multiple Time Series Analysis

New Introduction to Multiple Time Series Analysis by Helmut Lütkepohl
English | PDF(True) | 2005 | 765 Pages | ISBN : 3540401725 | 13.3 BMB

This reference work and graduate level textbook considers a wide range of models and methods for analyzing and forecasting multiple time series. The models covered include vector autoregressive, cointegrated,vector autoregressive moving average, multivariate ARCH and periodic processes as well as dynamic simultaneous equations and state space models. Least squares, maximum likelihood and Bayesian methods are considered for estimating these models.
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Bootstrap Tests for Regression Models (Repost)

Posted By: AvaxGenius
Date: 2 Feb 2022 05:43:46
Bootstrap Tests for Regression Models (Repost)

Bootstrap Tests for Regression Models by Leslie Godfrey
English | PDF | 2009 | 343 Pages | ISBN : 0230202306 | 1 MB

An accessible discussion examining computationally-intensive techniques and bootstrap methods, providing ways to improve the finite-sample performance of well-known asymptotic tests for regression models. This book uses the linear regression model as a framework for introducing simulation-based tests to help perform econometric analyses.
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Economic and Financial Modeling with Mathematica®

Posted By: AvaxGenius
Date: 14 Nov 2021 14:12:36
Economic and Financial Modeling with Mathematica®

Economic and Financial Modeling with Mathematica® by Hal R. Varian
English | PDF | 1993 | 480 Pages | ISBN : 0387978828 | 37.4 MB

Mathematica is a computer program (software) for doing symbolic, numeric and graphical analysis of mathematical problems. In the hands of economists, financial analysts and other professionals in econometrics and the quantitative sector of economic and financial modeling, it can be an invaluable tool for modeling and simulation on a large number of issues and problems, besides easily grinding out numbers, doing statistical estimations and rendering graphical plots and visuals. Mathematica enables these individuals to do all of this in a unified environment.
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High-Dimensional Covariance Matrix Estimation: An Introduction to Random Matrix Theory

Posted By: AvaxGenius
Date: 29 Oct 2021 14:39:27
High-Dimensional Covariance Matrix Estimation: An Introduction to Random Matrix Theory

High-Dimensional Covariance Matrix Estimation: An Introduction to Random Matrix Theory by Aygul Zagidullina
English | PDF,EPUB | 2021 | 123 Pages | ISBN : 3030800644 | 16.2 MB

This book presents covariance matrix estimation and related aspects of random matrix theory. It focuses on the sample covariance matrix estimator and provides a holistic description of its properties under two asymptotic regimes: the traditional one, and the high-dimensional regime that better fits the big data context.
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