Abstract:
We identify the sufficient reduction for the Principal Fitted Components model under mild conditions which generalize those considered in previous works. We give a short proof of the main result based on the d-separation for directed acyclic graphs (DAGs), linking two areas that, to our knowledge, have not been linked before in statistics. © 2018
Referencias:
- Azzalini, A., Dalla Valle, A., The multivariate skew-normal distribution (1996) Biometrika, 83 (4), pp. 715-726
- Basu, D., Pereira, C.A.B., Conditional independence in statistics (1983) Sankhyā, 45, pp. 324-337
- Bura, E., Duarte, S., Forzani, L., Sufficient reductions in regressions with exponential family inverse predictors (2016) J. Amer. Statist. Assoc., 111 (515), pp. 1313-1329
- Bura, E., Forzani, L., Sufficient reductions in regressions with elliptically contoured inverse predictors (2015) J. Amer. Statist. Assoc., 110 (509), pp. 420-434
- Cook, R.D., Fisher lecture: dimension reduction in regression (2007) Statist. Sci., 22 (1), pp. 1-26
- Cook, R.D., Forzani, L., Principal fitted components for dimension reduction in regression (2008) Statist. Sci., 23 (4), pp. 485-501
- Cook, R.D., Li, B., Chiaromonte, F., Envelope models for parsimonious and efficient multivariate linear regression (2010) Statist. Sinica, 20 (3), pp. 927-960
- Cook, R.D., Ni, L., Sufficient dimension reduction via inverse regression (2005) J. Amer. Statist. Assoc., 100 (470), pp. 410-428
- Cook, R.D., Weisberg, S., Comment (1991) J. Amer. Statist. Assoc., 86 (414), pp. 328-332
- Kelker, D., Distribution theory of spherical distributions and a location-scale parameter generalization (1970) Sankhyā, pp. 419-430
- Li, B., Artemiou, A., Li, L., Principal support vector machines for linear and nonlinear sufficient dimension reduction (2011) Ann. Statist., 39 (6), pp. 3182-3210
- Li, B., Wang, S., On directional regression for dimension reduction (2007) J. Amer. Statist. Assoc., 102 (479), pp. 997-1008
- Li, K.-C., Sliced inverse regression for dimension reduction (1991) J. Amer. Statist. Assoc., 86 (414), pp. 316-327
- Li, K.-C., On principal hessian directions for data visualization and dimension reduction: another application of stein's lemma (1992) J. Amer. Statist. Assoc., 87 (420), pp. 1025-1039
- Pearl, J., (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference, The Morgan Kaufmann Series in Representation and Reasoning, p. xx+552. , Morgan Kaufmann, San Mateo, CA
- Pearl, J., Causality (2009), p. xvi+384. , Cambridge University Press, Cambridge Models, reasoning, and inference; Scrucca, L., Model-based sir for dimension reduction (2011) Comput. Statist. Data Anal., 55 (11), pp. 3010-3026
- Szretter, M.E., Yohai, V.J., The sliced inverse regression algorithm as a maximum likelihood procedure (2009) J. Statist. Plann. Inference, 139 (10), pp. 3570-3578
- Verma, T., Pearl, J., Causal networks: semantics and expressiveness (1988) Proceedings of the Fourth Workshop on Uncertainty in Artificial Intelligence, Minneapolis, MN, Mountain View, CA, pp. 352-359
- Wasserman, L., All of Statistics: A Aoncise Course in Statistical Inference (2013), Springer Science & Business Media
Citas:
---------- APA ----------
(2019)
. Using DAGs to identify the sufficient dimension reduction in the Principal Fitted Components model. Statistics and Probability Letters, 145, 317-320.
http://dx.doi.org/10.1016/j.spl.2018.08.008---------- CHICAGO ----------
Szretter Noste, M.E.
"Using DAGs to identify the sufficient dimension reduction in the Principal Fitted Components model"
. Statistics and Probability Letters 145
(2019) : 317-320.
http://dx.doi.org/10.1016/j.spl.2018.08.008---------- MLA ----------
Szretter Noste, M.E.
"Using DAGs to identify the sufficient dimension reduction in the Principal Fitted Components model"
. Statistics and Probability Letters, vol. 145, 2019, pp. 317-320.
http://dx.doi.org/10.1016/j.spl.2018.08.008---------- VANCOUVER ----------
Szretter Noste, M.E. Using DAGs to identify the sufficient dimension reduction in the Principal Fitted Components model. Stat. Probab. Lett. 2019;145:317-320.
http://dx.doi.org/10.1016/j.spl.2018.08.008