Rank regression for modeling bus dwell time in the presence of censored observations
Bus dwell time estimation is very important for public transport planners and bus operators. Modeling bus dwell time is challenging, both theoretically and computationally, in the presence of censored observations. Common linear regression models are parametric models...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Academy of Sciences Malaysia,Akademi Sains Malaysia
2019
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| Online Access: | http://psasir.upm.edu.my/id/eprint/81928/ http://psasir.upm.edu.my/id/eprint/81928/1/Rank%20regression.pdf |
| _version_ | 1848859200441024512 |
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| author | Karimi, Mostafa Ibrahim, Noor Akma |
| author_facet | Karimi, Mostafa Ibrahim, Noor Akma |
| author_sort | Karimi, Mostafa |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Bus dwell time estimation is very important for public transport planners and bus operators. Modeling bus dwell time is challenging, both theoretically and computationally, in the presence of censored observations. Common linear regression models are parametric models that involve assumptions that are difficult to satisfy in applications. Rank regression based on the accelerated failure time model is a semiparametric model that does not involve assumptions about the model variables or the model error terms. Hence, this paper proposes rank estimators for modeling bus dwell time on the basis of Gehan and log-rank weight functions. An iterative algorithm is introduced that involves a monotone estimating function of the model parameter, and its minimization is a computationally simple optimization problem. A resampling technique is used for estimating the distribution of the rank estimator through its empirical distribution. The proposed methodology is performed on a real data set to assess the efficiency of the rank estimators in applications. The results illustrate that the proposed parameter estimators are fairly unbiased and censored observations do not significantly impact the performance of the rank estimators |
| first_indexed | 2025-11-15T12:25:34Z |
| format | Article |
| id | upm-81928 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T12:25:34Z |
| publishDate | 2019 |
| publisher | Academy of Sciences Malaysia,Akademi Sains Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-819282020-10-17T19:45:24Z http://psasir.upm.edu.my/id/eprint/81928/ Rank regression for modeling bus dwell time in the presence of censored observations Karimi, Mostafa Ibrahim, Noor Akma Bus dwell time estimation is very important for public transport planners and bus operators. Modeling bus dwell time is challenging, both theoretically and computationally, in the presence of censored observations. Common linear regression models are parametric models that involve assumptions that are difficult to satisfy in applications. Rank regression based on the accelerated failure time model is a semiparametric model that does not involve assumptions about the model variables or the model error terms. Hence, this paper proposes rank estimators for modeling bus dwell time on the basis of Gehan and log-rank weight functions. An iterative algorithm is introduced that involves a monotone estimating function of the model parameter, and its minimization is a computationally simple optimization problem. A resampling technique is used for estimating the distribution of the rank estimator through its empirical distribution. The proposed methodology is performed on a real data set to assess the efficiency of the rank estimators in applications. The results illustrate that the proposed parameter estimators are fairly unbiased and censored observations do not significantly impact the performance of the rank estimators Academy of Sciences Malaysia,Akademi Sains Malaysia 2019 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/81928/1/Rank%20regression.pdf Karimi, Mostafa and Ibrahim, Noor Akma (2019) Rank regression for modeling bus dwell time in the presence of censored observations. ASM Science Journal, 12 (spec.5). pp. 184-189. ISSN 1823-6782 |
| spellingShingle | Karimi, Mostafa Ibrahim, Noor Akma Rank regression for modeling bus dwell time in the presence of censored observations |
| title | Rank regression for modeling bus dwell time in the presence of censored observations |
| title_full | Rank regression for modeling bus dwell time in the presence of censored observations |
| title_fullStr | Rank regression for modeling bus dwell time in the presence of censored observations |
| title_full_unstemmed | Rank regression for modeling bus dwell time in the presence of censored observations |
| title_short | Rank regression for modeling bus dwell time in the presence of censored observations |
| title_sort | rank regression for modeling bus dwell time in the presence of censored observations |
| url | http://psasir.upm.edu.my/id/eprint/81928/ http://psasir.upm.edu.my/id/eprint/81928/1/Rank%20regression.pdf |