@article{c2e77051296a473d974823322772492a,
title = "Influence diagnostics in possibly asymmetric circular-linear multivariate regression models",
abstract = "Distributional studies and regression models have played important roles in statistical analysis of circular data. Asymmetric circular-linear multivariate regression models (SenGupta and Ugwuowo Environ. Ecol. Stat. 13(3), 299-309 2006) are motivated by and applied to predict some environmental characteristics based on both circular and linear predictors. In this paper, we consider a likelihood approach (Cook J. R. Stat. Soc. Ser. B Stat Methodol. 48(2), 133-169 1986) to study influence diagnostic analysis for these models, using the maximum likelihood estimation and influence diagnostics methods. The observed information matrices and normal curvatures are derived. Simulated and real data examples are then provided to illustrate our approach and establish the utility of our results.",
keywords = "Angular-linear dependency, Maximum likelihood estimation, Solar energy data",
author = "Shuangzhe LIU and Tiefeng Ma and Ashis SenGupta and Kunio Shimizu and Minzhen Wang",
note = "Funding Information: We are deeply thankful to the Editor and two reviewers for their constructive comments which led to an improved version of the manuscript. We are also grateful to Prof. F. Ugwuowo for kindly providing the data set used in this paper. We would like to thank the participants for their feedback and remarks, as the paper was presented at an ISM Symposium on Environmental Statistics, Tokyo and at the 57th Annual Meeting of the Australian Mathematical Society, Sydney. We would also like to acknowledge that this work was initiated and conducted when we met at Keio University and the Institute of Statistical Mathematics, Japan, and at University of Canberra, Australia. We are very grateful to the institutions and the colleagues for their kind support and hospitality. This study was supported by the National Natural Science Foundation of ChinaNSFC(No. 11471264, 11401148, 11571282). Publisher Copyright: {\textcopyright} 2016, Indian Statistical Institute. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.",
year = "2017",
doi = "10.1007/s13571-016-0116-8",
language = "English",
volume = "79",
pages = "76--93",
journal = "Sankhya: The Indian Journal of Statistics",
issn = "0581-572X",
publisher = "Indian Statistical Institute",
number = "1",
}