Information geometry of Statistical inference with selective sample

S. Eguchi, ISM & GUAS

This talk is a part of co-work withJ. Copas, University of Warwick

  Local Sensitivity Approximation

for Selectivity Bias.

J. Copas and S. Eguchi

J. Royal Statist. Soc. B, 63 (2001), 871-895. (http://www.ism.ac.jp/~eguchi/recent_preprint.html)

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Future problem

bounds bleinterpretaGet

for with of modelnear for the

inference theCompare

modelof neiborhoodTubular

analysis ySensitivit

bias Selection

tyIgnorabili

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Begg,C.B., Satagopan, J.M. and Berwick, M.(1998) A new strategy for evaluating the impact of epidemiologic risk factors for cancer with application to melanoma. J. Am. Statist. Assoc., 93, 415-426.

Bowater, R.J.,Copas, J.B., Machado, O.A. and Davis, A.C. (1996) Hearing impairmentand the log-normal distribution. Applied Statistics, 45, 203-217.

Chambers, R.L.and Welsh, A.H. (1993) Log-linear models for survey data with non-ignorable non-response. J.Roy.Statist.Soc., B, 55, 157-170.

Copas, J. B.and Li, H. G. (1997) Inference for non-random samples (with discussion). J. Roy. Statist. Soc.,B, 59 ,55-95.

Copas, J.B. and Marshall, P. (1998) The offender group reconviction scale:a statisticalreconviction score for use by probation offers. Applie Statistics, 47, 159-171.

References

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Rosenbaum, P.R. and Rubin,D.B.(1983)Assessing sensitivity to an unobserved binary covariate in an observational study with binary outcome. J. Roy. Statist. Soc., B, 45, 212-218.

Scharfstein, D,O., Rotnitzy, A. and Robins, J. M. (1999) Adjusting for non-ignorable drop-out using semiparametric nonresponse models (with discussion). J. Amer. Statist.Assoc.,94, 1096-1146.

Schlesselman,J.J.(1978)Assessing effects of confounding variables. Am. J. Epidemiology, 108, 3-8.

White,H.(1982)Maximum likelihood estimation of misspecified models. Econometrica, 50, 1-26.