*Spline Regression Models shows the nuts-and-bolts of using dummy variables to formulate and estimate various spline regression models.*

**Author**: Lawrence C. Marsh

**Publisher:** SAGE

**ISBN:** 0761924205

**Category:** Mathematics

**Page:** 69

**View:** 727

*Spline Regression Models shows the nuts-and-bolts of using dummy variables to formulate and estimate various spline regression models.*

**Author**: Lawrence C. Marsh

**Publisher:** SAGE

**ISBN:** 0761924205

**Category:** Mathematics

**Page:** 69

**View:** 727

*Spline Regression Models shows the nuts-and-bolts of using dummy variables to formulate and estimate various spline regression models.*

**Author**: Lawrence C. Marsh

**Publisher:** SAGE Publications, Incorporated

**ISBN:** 0761924205

**Category:** Social Science

**Page:** 80

**View:** 699

*This book introduces methods of robust optimization in multivariate adaptive regression splines (MARS) and Conic MARS in order to handle uncertainty and non-linearity.*

**Author**: Ayşe Özmen

**Publisher:** Springer

**ISBN:** 9783319308005

**Category:** Business & Economics

**Page:** 139

**View:** 105

*The user of linear spline regression chooses the values of the regressor X but not
the Y values that define the “joints” in a spline model. In Figure 12.9, these are
marked J1, J2, and J3. These could be chosen after examining a scatterplot, ...*

**Author**: Richard B. Darlington

**Publisher:** Guilford Publications

**ISBN:** 9781462521135

**Category:** Social Science

**Page:** 661

**View:** 257

*Parametric splines are piecewise polynomials and can be fitted using any
existing regression program after the constructed predictors are computed.
Spline regression is equally suitable to multiple linear regression, survival
models, and ...*

**Author**: Frank E. Harrell , Jr.

**Publisher:** Springer

**ISBN:** 9783319194257

**Category:** Mathematics

**Page:** 582

**View:** 262

*Table 6.5 OLS regression with linear and natural splines (Example 2: Working *

hours) Linear splines Natural splines M1 M2 M3 M4 M5 age: Spline 1 –0.198 –

0.881 0.229∗ –0.043 –0.088 (0.193) (0.682) (0.091) (0.134) (0.165) Spline 2 ...

**Author**: Henning Best

**Publisher:** SAGE

**ISBN:** 9781473908352

**Category:** Reference

**Page:** 424

**View:** 643

*Parametric splines are piecewise polynomials and can be fitted using any
existing regression program after the constructed predictors are computed.
Spline regression is equally suitable to multiple linear regression, survival
models, and ...*

**Author**: Frank E. Harrell

**Publisher:** Springer Science & Business Media

**ISBN:** 9781475734621

**Category:** Mathematics

**Page:** 572

**View:** 224

*This edition contains discussions of boundary corrections for trigonometric series estimators; detailed asymptotics for polynomial regression; testing goodness-of-fit; estimation in partially linear models; practical aspects, problems and ...*

**Author**: Randall L. Eubank

**Publisher:** CRC Press

**ISBN:** 9781482273144

**Category:** Mathematics

**Page:** 360

**View:** 697

*In this thesis , we study the applications of free - knot spline in two nonlinear
regression models with multiple responses : ( 1 ) Self - modeling nonlinear
regression – Repeated measurements are observed at consecutive times ( or
ages ) for a ...*

**Author**: Yuh-Wen Soo

**Publisher:**

**ISBN:** WISC:89035656008

**Category:**

**Page:** 400

**View:** 775

*This book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models when they are nonlinear.*

**Author**: George J. Knafl

**Publisher:** Springer

**ISBN:** 9783319339467

**Category:** Medical

**Page:** 372

**View:** 524

*Smoothing Splines: Methods and Applications covers basic smoothing spline models, including polynomial, periodic, spherical, t*

**Author**: Yuedong Wang

**Publisher:** CRC Press

**ISBN:** 9781420077568

**Category:** Mathematics

**Page:** 384

**View:** 488

*Failure time analysis is a form of regression analysis where the time until an
event occurs is of interest. The event is generically referred to as failure in this
thesis, the observational units are referred to as individuals. Unlike most
regression ...*

**Author**: Matthias Kaeding

**Publisher:** Springer

**ISBN:** 9783658083939

**Category:** Mathematics

**Page:** 110

**View:** 839

*NONLINEAR MODELING OF MULTIVARIATE AND CATEGORICAL TIME
SERIES USING MULTIVARIATE ADAPTIVE REGRESSION SPLINES PETER
A W LEWIS Department of Operations Research , Naval Postgraduate School
Monterey ...*

**Author**: Howell Tong

**Publisher:** World Scientific

**ISBN:** 9810213530

**Category:** Mathematics

**Page:** 223

**View:** 425

*as a basis function in a combined substantive-empirical approach that uses re-
gression splines as the other basis ... which use the parametric regression
models of Chapter 4 or the nonparametric regression models of this chapter, are
...*

**Author**: Tze Leung Lai

**Publisher:** Springer Science & Business Media

**ISBN:** 9780387778266

**Category:** Business & Economics

**Page:** 356

**View:** 764

*An introductory guide to smoothing techniques, semiparametric estimators, and their related methods, this book describes the methodology via a selection of carefully explained examples and data sets.*

**Author**: Luke John Keele

**Publisher:** John Wiley & Sons

**ISBN:** 0470998121

**Category:** Mathematics

**Page:** 230

**View:** 585

*... ( 2 ) The Overfitting Is Based on Spline Functions The spline regression model
differs from the piecewise linear regression model mainly by the fact that in the
spline case adjacent regression lines ( and its derivatives ) are required to
intersect ...*

**Author**: Peter Hackl

**Publisher:** Vandehoeck & Rupprecht

**ISBN:** STANFORD:36105038907957

**Category:** Regression analysis

**Page:** 132

**View:** 490

*... provided a mathematically gentler introduction to the field through regression
models that are largely univariate. Much has happened in the past decade, and
more has been done with the penalty method than just regression. In this book.*

**Author**: Chong Gu

**Publisher:** Springer Science & Business Media

**ISBN:** 0387953531

**Category:** Mathematics

**Page:** 289

**View:** 778