Estimations of means and variances in a Markov linear model

  • Abraham Gutierrez Sanchez (Speaker)

Activity: Talk or presentationTalk at conference or symposiumScience to science


Multivariate regression models and ANOVA are probably the most frequently applied methods
of all statistical analyses. In the second chapter of this thesis, we propose an alternative
to the classic approaches that do not assume homoscedasticity or normality of the error term
but assumes that a Markov chain can describe the covariates’ correlations. This approach
transforms the dependent covariate using a change of measure to independent covariates. The
transformed estimates allow a pairwise comparison of the mean and variance of the contribution
of different values of the covariates. We show that under standard moment conditions,
the estimators are asymptotically normally distributed. Additionally, we test our method with
simulated data and apply it to several classic data sets.
Period10 Sep 2020
Event title8th Austrian Stochastic Days: Graz 2020
Event typeConference
LocationGraz, Austria
Degree of RecognitionNational


  • Statistik
  • Stochastik
  • Markov chains

ASJC Scopus subject areas

  • Applied Mathematics
  • Statistics and Probability