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Books on the topic 'Least Square Regression Method'

Author: Grafiati
Published: 10 January 2023
Last updated: 28 January 2023

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1

Racine, J. S. Semiparamteric estimation in the presence of heteroskedasticity of unknown form. Toronto, Ont: Dept. of Economics, York University, 1989.

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2

Tiedeman, Claire R. Application of nonlinear-regression methods to a ground-water flow model of the Albuquerque Basin, New Mexico. Menlo Park, Calif: U.S. Dept. of the Interior, U.S. Geological Survey, 1998.

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3

Tiedeman, Claire R. Application of nonlinear-regression methods to a ground-water flow model of the Albuquerque Basin, New Mexico. Menlo Park, Calif: U.S. Dept. of the Interior, U.S. Geological Survey, 1998.

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4

Tiedeman, Claire R. Application of nonlinear-regression methods to a ground-water flow model of the Albuquerque Basin, New Mexico. Menlo Park, Calif: U.S. Dept. of the Interior, U.S. Geological Survey, 1998.

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5

Tiedeman, Claire R. Application of nonlinear-regression methods to a ground-water flow model of the Albuquerque Basin, New Mexico. Menlo Park, Calif: U.S. Dept. of the Interior, U.S. Geological Survey, 1998.

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6

Tiedeman, Claire R. Application of nonlinear-regression methods to a ground-water flow model of the Albuquerque Basin, New Mexico. Menlo Park, Calif: U.S. Dept. of the Interior, U.S. Geological Survey, 1998.

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7

Tiedeman, Claire R. Application of nonlinear-regression methods to a ground-water flow model of the Albuquerque Basin, New Mexico. Menlo Park, Calif: U.S. Dept. of the Interior, U.S. Geological Survey, 1998.

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8

Tiedeman, Claire R. Application of nonlinear-regression methods to a ground-water flow model of the Albuquerque Basin, New Mexico. Menlo Park, Calif: U.S. Dept. of the Interior, U.S. Geological Survey, 1998.

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9

Grafarend, Erik. Linear and Nonlinear Models: Fixed effects, random effects, and total least squares. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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10

Yang, Keming, ed. Categorical Data Analysis. Los Angeles, USA: SAGE Publications Ltd, 2014.

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11

NANDE-VÁZQUEZ, Edgard Alfredo, Teodoro REYES-FONG, and Omar Alejandro PÉREZ-CRUZ. The Generalized Least Squares Method (GMM) as a tool for causal analysis of spending, budget management and electoral results. ECORFAN, 2021. http://dx.doi.org/10.35429/b.2021.8.1.130.

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In the different fields of science, many times, there is a need to estimate the associations between variables, as an approach to understanding the interaction of one as a function of the others. It is usually done by applying restrictive models, such as analysis of variance and linear regression. This type of analysis requires that the dependent variable be continuous, have a normal and constant distribution of the mean and variance. However, when the dependent variable is discrete or categorical, the linear model is not viable. Faced with this impediment, the theory of linear models arises and is expanded to broader categories, which have been called Generalized Linear Models. This category assumes that all distribution functions are exponential, in which the normal distribution is located. In this sense, in this research, Generalized Least Squares methods were applied in their various variants: of moments, ordinary and feasible. These models allow calculating the parameters of models in which the dependent variable has a Poisson or multinomial distribution. In such a way that the Generalized Least Squares serve as a tool to analyze the effect of the elections on public spending and its relationship with the electoral results, analyzing the variables of a budgetary nature, derived from the possibility that the government in power continues or is re-elected. For this, data related to the states and municipalities of México in the period 2007 to 2019 are used.
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12

Witkov, Carey, and Keith Zengel. Chi-Squared Data Analysis and Model Testing for Beginners. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198847144.001.0001.

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This book is the first to make chi-squared model testing, one of the data analysis methods used to discover the Higgs boson and gravitational waves, accessible to undergraduate students in introductory physics laboratory courses. By including uncertainties in the curve fitting, chi-squared data analysis improves on the centuries old ordinary least squares and linear regression methods and combines best fit parameter estimation and model testing in one method. A toolkit of essential statistical and experimental concepts is developed from the ground up with novel features to interest even those familiar with the material. The presentation of one- and two-parameter chi-squared model testing, requiring only elementary probability and algebra, is followed by case studies that apply the methods to simple introductory physics lab experiments. More challenging topics, requiring calculus, are addressed in an advanced topics chapter. This self-contained and student-friendly introduction to chi-squared analysis and model testing includes a glossary, end-of-chapter problems with complete solutions, and software scripts written in several popular programming languages, that the reader can use for chi-squared model testing. In addition to introductory physics lab students, this accessible introduction to chi-squared analysis and model testing will be of interest to all who need to learn chi-squared model testing, e.g. beginning researchers in astrophysics and particle physics, beginners in data science, and lab students in other experimental sciences.
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13

Kaufman, Robert L. Heteroskedasticity in Regression: Detection and Correction. SAGE Publications, Inc, 2013.

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14

Kaufman, Robert L. Heteroskedasticity in Regression: Detection and Correction. SAGE Publications, Incorporated, 2014.

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15

Handbook of Partial Least Squares: Concepts, Methods and Applications. Springer, 2016.

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16

Handbook of Partial Least Squares: Concepts, Methods and Applications. Springer, 2010.

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17

O. Görgülü and A. Akilli. Egg production curve fitting using least square support vector machines and nonlinear regression analysis. Verlag Eugen Ulmer, 2018. http://dx.doi.org/10.1399/eps.2018.235.

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18

Hall, Peter. Principal component analysis for functional data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.8.

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This article discusses the methodology and theory of principal component analysis (PCA) for functional data. It first provides an overview of PCA in the context of finite-dimensional data and infinite-dimensional data, focusing on functional linear regression, before considering the applications of PCA for functional data analysis, principally in cases of dimension reduction. It then describes adaptive methods for prediction and weighted least squares in functional linear regression. It also examines the role of principal components in the assessment of density for functional data, showing how principal component functions are linked to the amount of probability mass contained in a small ball around a given, fixed function, and how this property can be used to define a simple, easily estimable density surrogate. The article concludes by explaining the use of PCA for estimating log-density.
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19

(Editor), Vincenzo Esposito Vinzi, Wynne W. Chin (Editor), Joerg Henseler (Editor), and Huiwen Wang (Editor), eds. Handbook of Partial Least Squares: Concepts, Methods and Applications in Marketing and Related Fields (Springer Handbooks of Computational Statistics). Springer, 2008.

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20

Cardot, Hervé, and Pascal Sarda. Functional Linear Regression. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.2.

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This article presents a selected bibliography on functional linear regression (FLR) and highlights the key contributions from both applied and theoretical points of view. It first defines FLR in the case of a scalar response and shows how its modelization can also be extended to the case of a functional response. It then considers two kinds of estimation procedures for this slope parameter: projection-based estimators in which regularization is performed through dimension reduction, such as functional principal component regression, and penalized least squares estimators that take into account a penalized least squares minimization problem. The article proceeds by discussing the main asymptotic properties separating results on mean square prediction error and results on L2 estimation error. It also describes some related models, including generalized functional linear models and FLR on quantiles, and concludes with a complementary bibliography and some open problems.
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21

Halperin, Sandra, and Oliver Heath. 16. Patterns of Association. Oxford University Press, 2017. http://dx.doi.org/10.1093/hepl/9780198702740.003.0016.

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This chapter discusses the principles of bivariate analysis as a tool for helping researchers get to know their data and identify patterns of association between two variables. Bivariate analysis offers a way of establishing whether or not there is a relationship between two variables, a dependent variable and an independent variable. With bivariate analysis, theoretical expectations can be compared against evidence from the real world to see if the theory is supported by what is observed. The chapter examines the pattern of association between dependent and independent variables, with particular emphasis on hypothesis testing and significance tests. It discusses ordinary least squares (OLS) regression and cross-tabulation, two of the most widely used statistical analysis techniques in political research. Finally, it explains how to state the null hypothesis, calculate the chi square, and establishing the correlation between the dependent and independent variables.
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22

Veech, Joseph A. Habitat Ecology and Analysis. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198829287.001.0001.

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Habitat is crucial to the survival and reproduction of individual organisms as well as persistence of populations. As such, species-habitat relationships have long been studied, particularly in the field of wildlife ecology and to a lesser extent in the more encompassing discipline of ecology. The habitat requirements of a species largely determine its spatial distribution and abundance in nature. One way to recognize and appreciate the over-riding importance of habitat is to consider that a young organism must find and settle into the appropriate type of habitat as one of the first challenges of life. This process can be cast in a probabilistic framework and used to better understand the mechanisms behind habitat preferences and selection. There are at least six distinctly different statistical approaches to conducting a habitat analysis – that is, identifying and quantifying the environmental variables that a species most strongly associates with. These are (1) comparison among group means (e.g., ANOVA), (2) multiple linear regression, (3) multiple logistic regression, (4) classification and regression trees, (5) multivariate techniques (Principal Components Analysis and Discriminant Function Analysis), and (6) occupancy modelling. Each of these is lucidly explained and demonstrated by application to a hypothetical dataset. The strengths and weaknesses of each method are discussed. Given the ongoing biodiversity crisis largely caused by habitat destruction, there is a crucial and general need to better characterize and understand the habitat requirements of many different species, particularly those that are threatened and endangered.
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