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math.ST2026

Nonparametric quantile inference using Dirichlet processes

Nils Lid Hjort, Sonia Petrone

This chapter deals with nonparametric inference for quantiles from a Bayesian perspective, using the Dirichlet process. The posterior distribution for quantiles is characterised, e…

math.ST2026

Notes on the Theory of Statistical Symbol Recognition

Nils Lid Hjort

This document is a pdf generated from old plain-TeX files of 1986, of Nils Lid Hjort's `Notes on the Theory of Statistical Symbol Recogntion', a limited circulation 207-pages monog…

math.ST2026

Estimating the logistic regression equation when the model is incorrect

Nils Lid Hjort

Protesting mildly against the notion of an exactly correct parametric model the view is adopted that the logistic regression equation is merely an approximation to the underlying,…

math.ST2026

Bayesian bivariate survival estimation

J. K. Ghosh, Nils Lid Hjort, C. Messan +1

There is no easy extension of Kaplan-Meier and Nelson-Aalen estimators to the bivariate case, and estimating bivariate survival distributions nonparametrically is associated with v…

math.ST2026

The asymptotic effect of tuning parameters

Ingrid Dæhlen, Nils Lid Hjort, Ingrid Hobæk Haff

Tuning parameters are parameters involved in an estimating procedure for the purpose of reducing the risk of some other estimator. Examples include the degree of penalization in pe…

math.ST2026

On the last time and the number of times an estimator is more than epsilon from its target value

Nils Lid Hjort, Grete Fenstad

Suppose is a strongly consistent estimator for in some i.i.d. situation. Let and be respectively the last and the total nu…