Varying coefficient single-index regression model with missing responses under rank-based modelling

References

• Abebe, A., Bindele, H.F., Otlaadisa, M., and Makubate, B. (2021), ‘RobustEstimation of Single Index Models with Responses Missing at Random’, Statistical Papers, 62(5), 2195–2225.
   Web of Science ®Google Scholar
• Alaska Fisheries Science Center (2019a), ‘AFSC/ABL: Longline Sablefish Survey’, Accessed 2014-04-14. https://noaa-fisheries-afsc.data.socrata.com/dataset/AFSC-ABL-Longline-Sablefish-Survey/itxd-qjvg/data.
   Google Scholar
• Alaska Fisheries Science Center (2019b), ‘AFSC/RACE/GAP: RACEBASE Database’. http://www.afsc.noaa.gov/RACE/groundfish/survey_data/default.htm.
   Google Scholar
• Alaska Fisheries Science Center (2020), ‘AFSC/ABL: Longline Sablefish Survey from 1979-05-24 to 2017-08-27’, Online, NOAA National Centers for Environmental Information. https://inport.nmfs.noaa.gov/inport/item/17218.
   Google Scholar
• Anderson, P.J., and Piatt, J.F. (1999), ‘Community Reorganization in the Gulf of Alaska Following Ocean Climate Regime Shift’, Marine Ecology Progress Series, 189, 117–123.
   Web of Science ®Google Scholar
• Andrews, D.W.K. (1994), ‘Asymptotics for Semiparametric Econometric Models Via Stochastic Equicontinuity’, Econometrica, 62(1), 43–72.
   Web of Science ®Google Scholar
• Aydin, K., Barbeaux, S., Barnard, D., Chilton, L., Clark, B., Conners, M., Conrath, C., Dalton, M., Echave, K., Fritz, L., Furuness, M., Hanselman, D., Haynie, A., Hoff, J., Honkalehto, T., Hulson, P., Ianelli, J., Kotwicki, S., Lauth, R., Lowe, S., Lunsford, C., McGilliard, C., McKelvey, D., Nichol, D., Norcross, B., Ormseth, O., Palsson, W., Rodgveller, C., Rooper, C., Spencer, P., Spies, I., Stockhausen, W., Stram, D., TenBrink, T., Thompson, G., Tribuzio, C., Wilderbuer, T., and Williamson, N. (2015), ‘Stock Assessment and Fishery Evaluation Report for the Groundfish Resources of the Bering Sea/Aleutian Islands regions’, Technical Report, North Pacific Fishery Management Council.
   Google Scholar
• Bindele, H.F. (2017), ‘Strong Consistency of the General Rank Estimator’, Communications in Statistics – Theory and Methods, 46(2), 532–539.
   Web of Science ®Google Scholar
• Bindele, H.F., and Abebe, A. (2012), ‘Bounded Influence Nonlinear Signed-rank Regression’, Canadian Journal of Statistics, 40(1), 172–189.
   Web of Science ®Google Scholar
• Bindele, H.F., Abebe, A., and Meyer, K.N. (2018), ‘General Rank-based Estimation for Regression Single Index Models’, Annals of the Institute of Statistical Mathematics, 70(5), 1115–1146.
   Web of Science ®Google Scholar
• Brunner, E., and Denker, M. (1994), ‘Rank Statistics Under Dependent Observations and Applications to Factorial Designs’, Journal of Statistical Planning and Inference, 42(3), 353–378.
   Web of Science ®Google Scholar
• Delecroix, M., Hristache, M., and Patilea, V. (2006), ‘On Semiparametric Estimation in Single-index Regression’, Journal of Statistical Planning and Inference, 136(3), 730–769.
   Web of Science ®Google Scholar
• Einmahl, U., and Mason, D.M. (2005), ‘Uniform in Bandwidth Consistency of Kernel-type Function Estimators’, The Annals of Statistics, 33(3), 1380–1403.
   Web of Science ®Google Scholar
• Fan, J., and Gijbels, I. (1996), Local Polynomial Modelling and Its Applications: Monographs on Statistics and Applied Probability 66 (Vol. 66), USA: Chapman and Hall.
   Google Scholar
• Gu, L., and Yang, L. (2015), ‘Oracally Efficient Estimation for Single-index Link Function with Simultaneous Confidence Band’, Electronic Journal of Statistics, 9(1), 1540–1561.
   Web of Science ®Google Scholar
• Guo, X., Fang, Y., Zhu, X., Xu, W., and Zhu, L. (2018), ‘Semiparametric Double Robust and Efficient Estimation for Mean Functionals with Response Missing At Random’, Computational Statistics & Data Analysis, 128, 325–339.
   Web of Science ®Google Scholar
• Hájek, J., and Šidák, Z. (1967), Theory of Rank Tests, New York: Academic Press.
   Google Scholar
• Hájek, J., Šidák, Z., and Sen, P. (1999), Theory of Rank Tests, Probability and Mathematical Statistics, San Diego, USA: Academic Press.
   Google Scholar
• Hansen, B.E. (2008), ‘Uniform Convergence Rates for Kernel Estimation with Dependent Data’, Econometric Theory, 24(3), 726–748.
   Web of Science ®Google Scholar
• Hettmansperger, T.P., and McKean, J.W. (2011), Robust Nonparametric Statistical Methods (2nd ed.), Volume 119 of Monographs on Statistics and Applied Probability, Boca Raton, FL: CRC Press.
   Google Scholar
• Hollowed, A.B., Ianelli, J.N., and Livingston, P.A. (2000), ‘Including Predation Mortality in Stock Assessments: A Case Study for Gulf of Alaska Walleye Pollock’, ICES Journal of Marine Science, 57(2), 279–293.
   Web of Science ®Google Scholar
• Little, R.J.A. (1992), ‘Regression with Missing X's: A Review’, Journal of the American Statistical Association, 87(420), 1227–1237.
   Web of Science ®Google Scholar
• Little, R.J.A., and Rubin, D.B. (2002), Statistical Analysis with Missing Data (2nd ed.), Wiley Series in Probability and Statistics, Hoboken, NJ: Wiley-Interscience [John Wiley & Sons].
   Google Scholar
• Livingston, P.A. (1993), ‘Importance of Predation by Groundfish, Marine Mammals and Birds on Walleye Pollock Theragra Chalcogramma and Pacific Herring Clupea Pallasi in the Eastern Bering Sea’, Marine Ecology Progress Series, 102, 205–215.
   Web of Science ®Google Scholar
• Nagler, T., and Czado, C. (2016), ‘Evading the Curse of Dimensionality in Nonparametric Density Estimation with Simplified Vine Copulas’, Journal of Multivariate Analysis, 151, 69–89.
   Web of Science ®Google Scholar
• National Data Buoy Center (2018), ‘Meteorological and Oceanographic Data Collected from the National Data Buoy Center Coastal-Marine Automated Network (C-Man) and Moored (Weather) Buoys’. https://accession.nodc.noaa.gov/NDBC-CMANWx.
   Google Scholar
• National Marine Fisheries Service, NOAA (2014), ‘The Coastal & Oceanic Plankton Ecology, Production, & Observation Database’, Online. http://www.st.nmfs.noaa.gov/copepod/.
   Google Scholar
• Newey, W.K., and McFadden, D. (1994), ‘Large Sample Estimation and Hypothesis Testing’, Handbook of Econometrics, 4, 2111–2245.
   Google Scholar
• O'Brien, T. (2007), ‘Copepod: The Global Plankton Database. A Review of the 2007 Database Contents and New Quality Control Methodology’, Technical Report NOAA Tech. Memo. NMFS-F/ST-34, U.S. Dep. Commerce.
   Google Scholar
• Rao, B.P. (2009), ‘Conditional Independence, Conditional Mixing and Conditional Association’, Annals of the Institute of Statistical Mathematics, 61(2), 441–460.
   Web of Science ®Google Scholar
• Robins, J.M., Rotnitzky, A., and Zhao, L.P. (1994), ‘Estimation of Regression Coefficients when Some Regressors are Not Always Observed’, Journal of the American Statistical Association, 89(427), 846–866.
   Web of Science ®Google Scholar
• Rubin, D.B. (1976), ‘Inference and Missing Data’, Biometrika, 63(3), 581–592.
   Web of Science ®Google Scholar
• Serfling, R.J. (1980), Approximation Theorems of Mathematical Statistics, Wiley Series in Probability and Mathematical Statistics, New York: John Wiley & Sons Inc.
   Google Scholar
• Sun, W., Bindele, H.F., Abebe, A., and Correia, H. (2019), ‘General Local Rank Estimation for Single-index Varying Coefficient Models’, Journal of Statistical Planning and Inference, 202, 57–79.
   Web of Science ®Google Scholar
• Whitt, W. (2002), Stochastic-process Limits: An Introduction to Stochastic-process Limits and Their Application to Queues, NY, USA: Springer Science & Business Media.
   Google Scholar
• Xia, Y. (2006), ‘Asymptotic Distributions for Two Estimators of the Single-index Model’, Econometric Theory, 22(6), 1112–1137.
   Web of Science ®Google Scholar
• Xia, Y., Li, W., and Tong, H. (2007), ‘Threshold Variable Selection Using Nonparametric Methods’, Statistica Sinica, 17(1), 265.
   Web of Science ®Google Scholar
• Xue, L., and Wang, Q. (2012), ‘Empirical Likelihood for Single-index Varying-coefficient Models’, Bernoulli, 18(3), 836–856.
   Web of Science ®Google Scholar
• Yang, S.J., and Park, B.U. (2014), ‘Efficient Estimation for Partially Linear Varying Coefficient Models when Coefficient Functions Have Different Smoothing Variables’, Journal of Multivariate Analysis, 126, 100–113.
   Web of Science ®Google Scholar
• Zeng, P., He, T., and Zhu, Y. (2012), ‘A Lasso-type Approach for Estimation and Variable Selection in Single Index Models’, Journal of Computational and Graphical Statistics, 21(1), 92–109.
   Web of Science ®Google Scholar