Introduction to Parametric vs Nonparametric Modeling in Motorcycle Crash Analysis

Dec 24, 2025

150 likes | 260 Vues

This lecture explores the concepts of parametric and nonparametric modeling, using the motorcycle dataset from Silverman (1985) as a case study. The dataset examines the effects of stimulated impacts on motorcycles, focusing on the dependent variable of time after impact and the response variable of head acceleration in a post-mortem test object. The session showcases parametric modeling techniques, including scatter plots and polynomial fits, as well as nonparametric methods like regression spline fits, to analyze crash impact effects effectively.

armine

Télécharger la présentation

Introduction to Parametric vs Nonparametric Modeling in Motorcycle Crash Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

Lecture 1 INTRODUCTION

Parametric v.s. Nonparametric

Parametric Modeling • Examples
Parametric Modelling • Motorcycle data The motocycle data (Silverman 1985) • Collected to study the crashed effects after the motorcycles hit by a stimulated impact • Dependent variable: time after a stimulated impact with motorcycles • Response variable: head acceleration of a PTMO (post mortem human test object), capturing the crash effects
Parametric Modeling • Scatter Plot
Parametric Modelling • Polynoimal Fits