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.
| Original language | English |
|---|---|
| Pages (from-to) | 76-93 |
| Number of pages | 18 |
| Journal | Sankhya: The Indian Journal of Statistics |
| Volume | 79B |
| DOIs | |
| Publication status | Published - 2017 |
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Influence diagnostics in possibly asymmetric circular-linear multivariate regression models. / LIU, Shuangzhe; Ma, Tiefeng; SenGupta, Ashis; Shimizu, Kunio; Wang, Minzhen.
In: Sankhya: The Indian Journal of Statistics, Vol. 79B, 2017, p. 76-93.Research output: Contribution to journal › Article
TY - JOUR
T1 - Influence diagnostics in possibly asymmetric circular-linear multivariate regression models
AU - LIU, Shuangzhe
AU - Ma, Tiefeng
AU - SenGupta, Ashis
AU - Shimizu, Kunio
AU - Wang, Minzhen
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Angular-linear dependency
KW - Maximum likelihood estimation
KW - Solar energy data
UR - http://www.scopus.com/inward/record.url?scp=85022076883&partnerID=8YFLogxK
U2 - 10.1007/s13571-016-0116-8
DO - 10.1007/s13571-016-0116-8
M3 - Article
VL - 79B
SP - 76
EP - 93
JO - Sankhya: The Indian Journal of Statistics
JF - Sankhya: The Indian Journal of Statistics
SN - 0581-572X
ER -