Generalized Semiparametric Regression with Covariates Measured with Error

2009 ◽  
pp. 133-154 ◽  
Author(s):  
Thomas Kneib ◽  
Andreas Brezger ◽  
Ciprian M. Crainiceanu
Keyword(s):  
Semiparametric Regression

Semiparametric Regression

2003 ◽  
Author(s):  
David Ruppert ◽  
M. P. Wand ◽  
R. J. Carroll
Keyword(s):  
Semiparametric Regression

scholarly journals Quantifying the effects of passenger-level heterogeneity on transit journey times

2020 ◽  
Vol 1 ◽  
Author(s):  
Ramandeep Singh ◽  
Daniel J. Graham ◽  
Richard J. Anderson
Keyword(s):  
Large Scale ◽  
Semiparametric Regression ◽  
Related Factors ◽  
Network Characteristics ◽  
Journey Time ◽  
Time Variance ◽  
Assignment Algorithm ◽  
Rail Systems ◽  
Urban Rail ◽  
Potential Applications

Abstract In this paper, we apply flexible data-driven analysis methods on large-scale mass transit data to identify areas for improvement in the engineering and operation of urban rail systems. Specifically, we use data from automated fare collection (AFC) and automated vehicle location (AVL) systems to obtain a more precise characterisation of the drivers of journey time variance on the London Underground, and thus an improved understanding of delay. Total journey times are decomposed via a probabilistic assignment algorithm, and semiparametric regression is undertaken to disentangle the effects of passenger-specific travel characteristics from network-related factors. For total journey times, we find that network characteristics, primarily train speeds and headways, represent the majority of journey time variance. However, within the typically twice as onerous access and egress time components, passenger-level heterogeneity is more influential. On average, we find that intra-passenger heterogeneity represents 6% and 19% of variance in access and egress times, respectively, and that inter-passenger effects have a similar or greater degree of influence than static network characteristics. The analysis shows that while network-specific characteristics are the primary drivers of journey time variance in absolute terms, a nontrivial proportion of passenger-perceived variance would be influenced by passenger-specific characteristics. The findings have potential applications related to improving the understanding of passenger movements within stations, for example, the analysis can be used to assess the relative way-finding complexity of stations, which can in turn guide transit operators in the targeting of potential interventions.

The Zero-Inflated Negative Binomial Semiparametric Regression Model: Application to Number of Failing Grades Data

2021 ◽  
Author(s):  
Elton G. Aráujo ◽  
Julio C. S. Vasconcelos ◽  
Denize P. dos Santos ◽  
Edwin M. M. Ortega ◽  
Dalton de Souza ◽  
...  
Keyword(s):  
Regression Model ◽  
Negative Binomial ◽  
Semiparametric Regression ◽  
Model Application ◽  
Semiparametric Regression Model

scholarly journals Using the Self-Organising Map to Identify Regularities across Country-Specific Housing-Market Contexts

10.1068/b3186 ◽  
2005 ◽  
Vol 32 (1) ◽  
pp. 89-110 ◽  
Author(s):  
Tom Kauko
Keyword(s):  
Housing Market ◽  
Semiparametric Regression ◽  
The Self ◽  
Cross Sectional ◽  
Public And Private ◽  
Institutional Differences ◽  
Market Fundamentals ◽  
Self Organising Map ◽  
Public And Private Sector ◽  
Housing Submarkets

The aim of exploring and monitoring housing-market fundamentals (prices, dwelling features, area density, residents, and so on) on a macrolocational level relates to both public and private sector policymaking. Housing market segmentation (that is, the emergence of housing submarkets), a concept with increasing relevance, is defined as the differentiation of housing in terms of the income and preferences of the residents and in terms of administrative circumstances. In order to capture such segmentation empirically, the author applies a fairly new and emerging technique known as the ‘self-organising’ map (SOM), or ‘Kohonen map’. The SOM is a type of (artificial) neural network—a nonlinear and flexible (that is, nonparametric or semiparametric) regression and ‘machine learning’ technique. By utilising the ability of the SOM to visualise patterns, one can analyse various dimensions within the variation of the dataset. Segmentation may then be detected depending on the resulting patterns across the map layers, each of which represents the data variation for one input variable. Utilising an inductive modelling strategy, the author runs cross-sectional and nationwide data on the owner-occupied housing markets of Finland (documentation presented elsewhere), the Netherlands, and Hungary with the SOM technique. On the basis of the resulting configurations certain regularities (similarities and differences) across the three national contexts are identified. In all three cases the segments are determined by physical and institutional differences between the housing bundles and localities. The exercise demonstrates how the inductive SOM-based approach is well-suited for illustrating the contextual factors that determine housing market structure.

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