{"title":"Model fitting using partially ranked data","authors":"M. Alvo, XiuWen Duan","doi":"10.1080/10485252.2023.2176180","DOIUrl":null,"url":null,"abstract":"The importance of models for complete ranking data is well-established in the literature. Partial rankings, on the other hand, arise naturally when the set of objects to be ranked is relatively large. Partial rankings give rise to classes of compatible order preserving complete rankings. In this article, we define an exponential model for complete rankings and calibrate it on the basis of a random sample of partial rankings data. We appeal to the EM algorithm. The approach is illustrated in some simulations and in real data.","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"5 1","pages":"587 - 600"},"PeriodicalIF":0.8000,"publicationDate":"2023-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Nonparametric Statistics","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1080/10485252.2023.2176180","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0
Abstract
The importance of models for complete ranking data is well-established in the literature. Partial rankings, on the other hand, arise naturally when the set of objects to be ranked is relatively large. Partial rankings give rise to classes of compatible order preserving complete rankings. In this article, we define an exponential model for complete rankings and calibrate it on the basis of a random sample of partial rankings data. We appeal to the EM algorithm. The approach is illustrated in some simulations and in real data.
期刊介绍：
Journal of Nonparametric Statistics provides a medium for the publication of research and survey work in nonparametric statistics and related areas. The scope includes, but is not limited to the following topics:
Nonparametric modeling,
Nonparametric function estimation,
Rank and other robust and distribution-free procedures,
Resampling methods,
Lack-of-fit testing,
Multivariate analysis,
Inference with high-dimensional data,
Dimension reduction and variable selection,
Methods for errors in variables, missing, censored, and other incomplete data structures,
Inference of stochastic processes,
Sample surveys,
Time series analysis,
Longitudinal and functional data analysis,
Nonparametric Bayes methods and decision procedures,
Semiparametric models and procedures,
Statistical methods for imaging and tomography,
Statistical inverse problems,
Financial statistics and econometrics,
Bioinformatics and comparative genomics,
Statistical algorithms and machine learning.
Both the theory and applications of nonparametric statistics are covered in the journal. Research applying nonparametric methods to medicine, engineering, technology, science and humanities is welcomed, provided the novelty and quality level are of the highest order.
Authors are encouraged to submit supplementary technical arguments, computer code, data analysed in the paper or any additional information for online publication along with the published paper.
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