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2003
In this chapter, we consider point estimation of the parameters s ∈ ℝ P and σ2 ∈ (0, ∞) in the linear regression model $$y = X\beta + \varepsilon , \varepsilon \sim (0,{{\sigma }^{2}}{{I}_{n}}) $$ We will focus our attention to the ordinary least squares estimator $$ \hat \beta = (X'X)^{ - 1} X'y $$ and the least squares variance estimator
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In this chapter, we consider point estimation of the parameters s ∈ ℝ P and σ2 ∈ (0, ∞) in the linear regression model $$y = X\beta + \varepsilon , \varepsilon \sim (0,{{\sigma }^{2}}{{I}_{n}}) $$ We will focus our attention to the ordinary least squares estimator $$ \hat \beta = (X'X)^{ - 1} X'y $$ and the least squares variance estimator
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The Generalized Linear Regression Model
1995In Chapter 2 the generalized linear regression model is introduced as a special case (M = 1) of the multivariate (M-dimensional) model.
Calyampudi Radhakrishna Rao +1 more
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Judging the Significance of Multiple Linear Regression Models
Journal of Medicinal Chemistry, 2005It is common practice to calculate large numbers of molecular descriptors, apply variable selection procedures to reduce the numbers, and then construct multiple linear regression (MLR) models with biological activity. The significance of these models is judged using the usual statistical tests.
David J, Livingstone, David W, Salt
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The Assumptions of the Linear Regression Model
Transactions of the Institute of British Geographers, 1971The paper is prompted by certain apparent deficiences both in the discussion of the regression model in instructional sources for geographers and in the actual empirical application of the model by geographical writers. In the first part of the paper the assumptions of the two regression models, the 'fixed X' and the 'random X', are outlined in detail,
Michael A. Poole, Patrick N. O'Farrell
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Moving Beyond the Linear Regression Model
Journal of Management, 2014Heavy-tailed distributions occur often in empirical settings, making it difficult for management scholars to use linear regression models (LRMs) to investigate the nuanced relationships between dependent and predictor variables. Both frequentist and Bayesian quantile regression models (QRMs) are alternative techniques that can help management scholars
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The Simple Linear Regression Model
1991Up to now, we have been largely concerned with statistics in the context of only one variable. In the first five chapters of this book we discussed descriptive statistics of a single variable (except in Section 3.8 when we considered joint frequency distributions).
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Assumptions Behind the Linear Regression Model
SSRN Electronic Journal, 2010In a previous note, “Introduction to Least-Squares Modeling” (UVA-QA-0500), we have seen how least squares can be used to fit the simple linear model to historical data. The resulting model can then be used to forecast the next occurrence of Y, the dependent variable, for a given value of X, the independent variable.
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Nonparametric Estimation and Testing Linear Hypotheses in the Linear Regression Model
Mathematische Operationsforschung und Statistik, 1975A survey of the latest results in nonparametric hypotheses testing and in nonparametric estimation is given. At first, two main hypotheses usually handled by rank tests, these of randomness and of symmetry, are defined and the locally most powerful rank tests for them described. Aymptotic efficiency considerations are included. In the second part, four
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Antibody–drug conjugates: Smart chemotherapy delivery across tumor histologies
Ca-A Cancer Journal for Clinicians, 2022Paolo Tarantino +2 more
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