Flexible smoothing with B-splines and Penalties or P-splines P-splines = B-splines + Penalization Applications : Generalized Linear and non linear Modelling.

Slides:



Advertisements
Similar presentations
What Could We Do better? Alternative Statistical Methods Jim Crooks and Xingye Qiao.
Advertisements

Lecture 9: Smoothing and filtering data
CountrySTAT Team-I November 2014, ECO Secretariat,Teheran.
Copyright © 2006 The McGraw-Hill Companies, Inc. Permission required for reproduction or display. 1 ~ Curve Fitting ~ Least Squares Regression Chapter.
Pattern Recognition and Machine Learning: Kernel Methods.
Introduction to Smoothing Splines
Chapter 2: Lasso for linear models
Regression Analysis Using Excel. Econometrics Econometrics is simply the statistical analysis of economic phenomena Here, we just summarize some of the.
Data mining and statistical learning - lecture 6
MATH 685/ CSI 700/ OR 682 Lecture Notes
Basis Expansion and Regularization Presenter: Hongliang Fei Brian Quanz Brian Quanz Date: July 03, 2008.
1 Chapter 4 Interpolation and Approximation Lagrange Interpolation The basic interpolation problem can be posed in one of two ways: The basic interpolation.
ES 240: Scientific and Engineering Computation. InterpolationPolynomial  Definition –a function f(x) that can be written as a finite series of power functions.
Kernel methods - overview
統計計算與模擬 政治大學統計系余清祥 2003 年 6 月 9 日 ~ 6 月 10 日 第十六週:估計密度函數
Curve-Fitting Regression
Least Square Regression
The Islamic University of Gaza Faculty of Engineering Civil Engineering Department Numerical Analysis ECIV 3306 Chapter 17 Least Square Regression.
Modelling: Curves Week 11, Wed Mar 23
ECIV 301 Programming & Graphics Numerical Methods for Engineers Lecture 24 Regression Analysis-Chapter 17.
1 An Introduction to Nonparametric Regression Ning Li March 15 th, 2004 Biostatistics 277.
Empirical Estimation Review EconS 451: Lecture # 8 Describe in general terms what we are attempting to solve with empirical estimation. Understand why.
Copyright © 2006 The McGraw-Hill Companies, Inc. Permission required for reproduction or display. by Lale Yurttas, Texas A&M University Chapter 171 Least.
Biostatistics-Lecture 14 Generalized Additive Models Ruibin Xi Peking University School of Mathematical Sciences.
Curve fit noise=randn(1,30); x=1:1:30; y=x+noise ………………………………… [p,s]=polyfit(x,y,1);
CpE- 310B Engineering Computation and Simulation Dr. Manal Al-Bzoor
Empirical Modeling Dongsup Kim Department of Biosystems, KAIST Fall, 2004.
Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display. 1 Part 4 Curve Fitting.
Regression analysis Control of built engineering objects, comparing to the plan Surveying observations – position of points Linear regression Regression.
MECN 3500 Inter - Bayamon Lecture 9 Numerical Methods for Engineering MECN 3500 Professor: Dr. Omar E. Meza Castillo
Chapter 8 Curve Fitting.
Model Comparison for Tree Resin Dose Effect On Termites Lianfen Qian Florida Atlantic University Co-author: Soyoung Ryu, University of Washington.
Curve-Fitting Regression
CHAPTER 3 Model Fitting. Introduction Possible tasks when analyzing a collection of data points: Fitting a selected model type or types to the data Choosing.
1 Estimating the Term Structure of Interest Rates for Thai Government Bonds: A B-Spline Approach Kant Thamchamrassri February 5, 2006 Nonparametric Econometrics.
2011 COURSE IN NEUROINFORMATICS MARINE BIOLOGICAL LABORATORY WOODS HOLE, MA Introduction to Spline Models or Advanced Connect-the-Dots Uri Eden BU Department.
Jump to first page Chapter 3 Splines Definition (3.1) : Given a function f defined on [a, b] and a set of numbers, a = x 0 < x 1 < x 2 < ……. < x n = b,
Course 8 Contours. Def: edge list ---- ordered set of edge point or fragments. Def: contour ---- an edge list or expression that is used to represent.
Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display. 1 Part 4 Chapter 15 General Least Squares and Non- Linear.
Curve Fitting Introduction Least-Squares Regression Linear Regression Polynomial Regression Multiple Linear Regression Today’s class Numerical Methods.
Copyright © 2006 The McGraw-Hill Companies, Inc. Permission required for reproduction or display. 1 ~ Curve Fitting ~ Least Squares Regression.
Gaussian Process and Prediction. (C) 2001 SNU CSE Artificial Intelligence Lab (SCAI)2 Outline Gaussian Process and Bayesian Regression  Bayesian regression.
Fourier Approximation Related Matters Concerning Fourier Series.
1 Chapter 4 Interpolation and Approximation Lagrange Interpolation The basic interpolation problem can be posed in one of two ways: The basic interpolation.
Curves University of British Columbia CPSC 314 Computer Graphics Jan-Apr 2013 Tamara Munzner.
Statistics 350 Lecture 2. Today Last Day: Section Today: Section 1.6 Homework #1: Chapter 1 Problems (page 33-38): 2, 5, 6, 7, 22, 26, 33, 34,
Piecewise Polynomials and Splines
Chapter 7. Classification and Prediction
ECE3340 Numerical Fitting, Interpolation and Approximation
Curve-Fitting Spline Interpolation
Spatial Analysis Longley et al..
Statistical Methods For Engineers
CHAPTER 29: Multiple Regression*
Chapter 12 Curve Fitting : Fitting a Straight Line Gab-Byung Chae
© University of Wisconsin, CS559 Spring 2004
Linear Regression.
Regression Models - Introduction
The Regression Model Suppose we wish to estimate the parameters of the following relationship: A common method is to choose parameters to minimise the.
Spline Interpolation Class XVII.
Linear regression Fitting a straight line to observations.
Least Squares Fitting A mathematical procedure for finding the best-fitting curve to a given set of points by minimizing the sum of the squares of the.
Nonlinear Fitting.
Discrete Least Squares Approximation
Least Square Regression
Basis Expansions and Generalized Additive Models (2)
Introduction to Parametric Curve and Surface Modeling
SKTN 2393 Numerical Methods for Nuclear Engineers
政治大學統計系余清祥 2004年5月26日~ 6月7日 第十六、十七週:估計密度函數
Regression Models - Introduction
Theory of Approximation: Interpolation
Presentation transcript:

Flexible smoothing with B-splines and Penalties or P-splines P-splines = B-splines + Penalization Applications : Generalized Linear and non linear Modelling ; Density smoothing P-splines have their grounding in Classical regression methods and Generalized linear models Regression, Smoothing, Splines? B-splines P-splines?

Smoothing, Regression, Splines, B-splines P-splines? In statistics, linear regression refers to any approach to modeling the relationship between one or more variables denoted y and one or more variables denoted X, such that the model depends linearly on the unknown parameters to be estimated from the data. Such a model is called a "linear model."statisticsparametersestimateddata

Smoothing, Regression, Splines, B-splines P-splines?

Regression, Smoothing, Splines, B-splines P-splines? Linear model Generalized Linear model where x′iβ is the inner product between vectors xi and β.inner productvectors

Regression, Smoothing, Splines, B-splines P-splines? The term ei is the residual,. One method of estimation is ordinary least squares. This method obtains parameter estimates that minimize the sum of squared residuals, SSE: residualordinary least squaresresiduals

Regression, Smoothing, Splines, B-splines P-splines? Smoothing:In statistics and image processing, to smooth a data set is to create an approximating function that attempts to capture important patterns in the data, while leaving out noise or other fine-scale structures/rapid phenomena.statisticsimage processingdata setfunctionpatternsnoise

Regression, Smoothing, Splines, B-splines P-splines? Many different algorithms are used in smoothing. One of the most common algorithms is the "moving average", often used to try to capture important trends in repeated statistical surveys. In image processing and computer vision, smoothing ideas are used in scale-space representations.algorithmsmoving averagestatistical surveysimage processingcomputer visionscale-space

Regression, Smoothing, Splines, B-splines P-splines? Spline : Originally, a spline tool was a thin flexible strip of wood,metal or rubber used by draftsman to aid in drawing curved lines.

Regression, Smoothing, Splines, B-splines P-splines? a spline is a special function defined piecewise by polynomials. In interpolating problems, spline interpolation is often preferred to polynomial interpolation because it yields similar resultsfunction piecewisepolynomialsinterpolatingspline interpolationpolynomial interpolation

Splines Spline of degree zero

Splines A spline of degree 1

Splines A cubic spline

!B-splines! B-splines of degree 0

!B-splines A spline of degree 1

B-splines

B-splines are defined recursively

Properties of P-splines No boundary effects Are a straightforward extension of (generalized ) linear regression models Conserve moments like the mean and variances of the data and fit polynomial data exactly Computations and cross validation relatively inexpensive

Fitting curves with B-splines A fitted curve is the linear combination

Fitting curves with splines The corresponding SSE (quadratic error) is

O’Sullivan penalty Between 1986 and 1988 O’Sullivan introduced a penalty on the second derivative of the fitted curve;

Eilers and MarxPenalities Eilers /Marx penalty proposal based on finite differences

Applications Generalized linear modeling Density Smoothing Example 1 : Motorcycle crash helmet impact simulation data (Härdle 1994) head acceleration in g units at different times after impact. Smoothed with B- splines of degree 3 and a second order penalty

Example 1 Graph

Density Smoothing