Semiparametric topographical mixture models with symmetric errors
type: Dataset
description: Motivated by the analysis of a Positron Emission Tomography (PET) imaging data considered in Bowen et al. (2012), we introduce a semiparametric topographical mixture model able to capture the characteristics of dichotomous shifted response-type experiments. We propose a local estimation procedure, based on the symmetry of the local noise, for the proportion and locations functions involved in the proposed model. We establish under mild conditions the minimax properties and asymptotic normality of our estimators when Monte Carlo simulations are conducted to examine their finite sample performance. Finally a statistical analysis of the PET imaging data in Bowen et al. (2012) is illustrated for the proposed method.
issued: 2026-05-07T02:15:49.081298
language: en
modified: 2026-05-07T02:15:49.081303
contact point: Organization
  fn: CCSD
keyword: asymptotic-normality
keyword: consistency
keyword: contrast-estimator
keyword: finite-mixture-of-regressions
keyword: fourier-transform
keyword: identifiability
keyword: infoeu-reposemanticspreprint
keyword: inverse-problem
keyword: preprints-working-papers-
keyword: semiparametric-mixture-model
keyword: statthstatistics-statstatistics-theory-statth
keyword: symmetric-errors
landing page: https://hal.science/hal-00912964

Semiparametric topographical mixture models with symmetric errors
type: Distribution
format: HTML
issued: 2026-05-07T02:15:49.100621
modified: 2026-05-07T02:15:49.065487
access URL: https://hal.science/hal-00912964

test_moissonnage_selune
type: Agent