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Linear Regression
In this chapter we discuss one of the most important topics in statistics. It provides us with a way to determine an algebraic relationship between...
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Multiple Linear Regression Model
This chapter generalises the simple regression techniques of the previous chapter to the case where there are multiple possible explanatory...
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Simple Linear Regression Model
Chapter 17 kicks off Part V of the book on introduction to statistical modelling. It discusses the...
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Semi-Functional Partial Linear Quantile Regression Model with Randomly Censored Responses
Censored data with functional predictors often emerge in many fields such as biology, neurosciences and so on. Many efforts on functional data...
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A Nonparametric Model Checking Test for Functional Linear Composite Quantile Regression Models
This paper is focused on the goodness-of-fit test of the functional linear composite quantile regression model. A nonparametric test is proposed by...
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Hypothesis testing for points of impact in functional linear regression
Recently, there has been increased interest in issues related to functional linear regression models with points of impact. While the estimation of...
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Quantile Regression of Ultra-high Dimensional Partially Linear Varying-coefficient Model with Missing Observations
In this paper, we focus on the partially linear varying-coefficient quantile regression with missing observations under ultra-high dimension, where...
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Compressed Least Squares Algorithm of Continuous-Time Linear Stochastic Regression Model Using Sampling Data
In this paper, the authors consider a sparse parameter estimation problem in continuous-time linear stochastic regression models using sampling data....
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Linear, Logistic, and Kernel Regression
In machine learning, regression analysis refers to a process for estimating the relationships between dependent variables and independent variables....
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Strong Convergence Theorems Under Sub-linear Expectations and Its Applications in Nonparametric Regression Models
In this paper, we first study the complete convergence for arrays of rowwise widely orthant dependent random variables under sub-linear expectations....
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On estimation and prediction in spatial functional linear regression model
We consider a spatial functional linear regression, where a scalar response is related to a square-integrable spatial functional process. We use a...
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The Consistency of LSE Estimators in Partial Linear Regression Models under Mixing Random Errors
In this paper, we consider the partial linear regression model y i = x i β * + g ( t i ) + ε i , i = 1, 2, …, n , where ( x i , t i ) are known fixed design points, g ...
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Optimization: Regression
The problem central to this chapter, and the one that follows, is easy to state: given a function...
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Linear regression with partially mismatched data: local search with theoretical guarantees
Linear regression is a fundamental modeling tool in statistics and related fields. In this paper, we study an important variant of linear regression...
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A fuzzy linear regression model with autoregressive fuzzy errors based on exact predictors and fuzzy responses
This paper is an attempt to develop a novel linear regression model with autocorrelated fuzzy error terms and exact predictors and fuzzy responses....
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Statistical inference in the partial functional linear expectile regression model
As extensions of means, expectiles embrace all the distribution information of a random variable. The expectile regression is computationally...
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Linear Regression
This chapter introduces the model of linear regression and some simple algorithms to solve for it. In the simplest form it builds a linear model to...
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Development of Imputation Methods for Missing Data in Multiple Linear Regression Analysis
AbstractMissing data is a common issue in many domains of study. If this issue is disregarded, the erroneous conclusion may be reached. This study’s...