195 research outputs found
Karlis Poruks - Life in Edmonton
No full textKarlis Poruks reminisces about life In Edmonton and his experiences with his granparents and parents and family friends maintenence of the Latvian heritage including speakng Latvian at home, the continuation of Latvian cultural festivals and gatherings as well as latvian handicrafts.16.1 Latvian cultural festivals and celebrations, 15.1.3 Family life in Albert
Robustness of statistical methods for modeling paired count data using bivariate discrete distributions with general dependence structures
No full textBivariate Poisson models are appropriate for modeling paired count data.
However the bivariate Poisson model does not allow for negative dependence structure,
therefore it is necessary to consider alternatives, which can produce both positive
and negative dependence. A natural way is to consider copulas to generate
various bivariate discrete distributions. While such models exist in the literature, the
issue of choosing a suitable copula has been overlooked so far. Different copulas
lead to different structure, any copula misspecification can render the inference useless.
In this work, we consider bivariate Poisson models generated with a copula and
investigate its robustness under outliers contamination and model misspecification.
Particular focus is given on the robustness of copula related parameters
Robustness methods for modelling count data with general dependence structures
No full textBivariate Poisson models are appropriate for modelling paired count data. However, the bivariate Poisson model does not allow for a negative dependence structure. Therefore, it is necessary to consider alternatives. A natural way is to consider copulas to generate various bivariate discrete distributions. While such models exist in the literature, the issue of choosing a suitable copula has been overlooked so far. Different copulas lead to different structures and any copula misspecification can render the inference useless.
We consider bivariate Poisson models generated with a copula and investigate its robustness under outliers contamination and model misspecification. Particular focus is on the robustness of copula related parameters. English Premier League data are used to demonstrate the effectiveness of our approach
Lesins 6
No full textDr. and Mrs. Lesins with family friends, the Poruks at the University of Alberta campus farms, ca 1955. Mirdza Poruks on far left, Mrs. Irma Lesins in white, children Maija and Karlis Poruks, Dr. Karlis Lesins on right
Robustness methods for modelling count data with general dependence structures
No full textBivariate Poisson models are appropriate for modelling paired count
data. However, the bivariate Poisson model does not allow for a negative dependence
structure. Therefore, it is necessary to consider alternatives. A natural way is to consider
copulas to generate various bivariate discrete distributions. While such models
exist in the literature, the issue of choosing a suitable copula has been overlooked so
far. Different copulas lead to different structures and any copula misspecification can
render the inference useless. In this work, we consider bivariate Poisson models generated
with a copula and investigate its robustness under outliers contamination and
model misspecification. Particular focus is on the robustness of copula related parameters.
English Premier League data are used to demonstrate the effectiveness of our
approach
A Bayesian model for ranking hazardous sites
No full textThis paper proposes a methodology to rank dangerous road locations. The model is innovative in two respects. Firstly, it makes use of relevant information per accident location, including the total number of accidents, the number of fatalities, as well as the number of light and severe injuries. Secondly, the model includes a cost function to rank the sites with respect to their total expected cost to the society. Bayesian estimation for the model via a MCMC approach is proposed
Lesins 5
No full textNews clipping about Dr. Karlis Lesins, ethnic Latvian and University of Alberta cytogeneticist
A Bayesian model for ranking hazardous sites
No full textThis paper proposes a methodology to rank dangerous road locations. The model is innovative in two respects. Firstly, it makes use of relevant information per accident location, including the total number of accidents, the number of fatalities, as well as the number of light and severe injuries. Secondly, the model includes a cost function to rank the sites with respect to their total expected cost to the society. Bayesian estimation for the model via a MCMC approach is proposed
Corrigendum: A model for identifying and ranking dangerous accident locations: a case study in Flanders (vol 60, pg 457, 2006)
No full textThese days, road safety has become a major concern in most modern societies. In this respect, the determination of road locations that are more dangerous than others (black spots or also called sites with promise) can help in better scheduling road safety policies. The present paper proposes a multivariate model to identify and rank sites according to their total expected cost to the society. Bayesian estimation of the model via a Markov Chain Monte Carlo approach is discussed in this paper. To illustrate the proposed model, accident data from 23,184 accident locations in Flanders (Belgium) are used and a cost function proposed by the European Transport Safety Council IS adopted to illustrate the model. It is shown in the paper that the model produces insightful results that can help policy makers in prioritizing road infrastructure investments
Lesins 4
No full textIrma Lesins and Dr. Karlis Lesins on the University of Alberta farms. Dr. Lesins holding Maija Poruks
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