Podcasts about czado

Zapraszamy na specjalny odcinek poświęcony zmianie trenera. Marcin Brosz po 5 latach odchodzi z klubu, a w jego miejsce pojawia się Jan Urban. O tej zmianie porozmawialiśmy z gościem specjalnym, redaktorem Pawłem Czado. Zapraszamy!

Tym razem trochę inaczej niż zwykle. Dajemy Wam rozmowę z Pawłem Czado. Dziennikarzem, reporterem, autorem znakomitych książek o piłce nożnej. A w związku z tym, że nasz rozmówca związany jest ze Śląskiem, to szukamy odpowiedzi na pytanie, co stało się z futbolową potęgą klubów z tego regionu. Czy jest nadzieja dla śląskiej piłki? Przekonajcie się sami...

Dziś stajemy przed trudnym zadaniem. Spróbujemy w atrakcyjny sposób opowiedzieć o produkcie, który za atrakcyjny nie uchodzi, czyli o polskiej piłkarskiej ekstraklasie. Czy wciąż warto chodzić na mecze ekstraklasy? Czy są one emocjonujące? Czy może kochamy tę ligę tylko dla tego, że jest po prostu nasza? Szef redakcji sportowej "Gazety Wyborczej" Marcin Wesołek rozmawia z Pawłem Czado, dziennikarzem naszej katowickiej redakcji. Więcej odcinków na https://wyborcza.pl/podcast

This paper considers the problem of modeling migraine severity assessments and their dependence on weather and time characteristics. Since ordinal severity measurements arise from a single patient dependencies among the measurements have to be accounted for. For this the autore- gressive ordinal probit (AOP) model of Müller and Czado (2004) is utilized and fitted by a grouped move multigrid Monte Carlo (GM-MGMC) Gibbs sampler. Initially, covariates are selected using proportional odds models ignoring this dependency. Model fit and model comparison are discussed. The analysis shows that humidity, windchill, sunshine length and pressure differences have an effect in addition to a high dependence on previous measurements. A comparison with proportional odds specifications shows that the AOP models are preferred.

This paper considers the problem of modeling migraine severity assessments and their dependence on weather and time characteristics. Since ordinal severity measurements arise from a single patient, dependencies among the measurements have to be accounted for. For this the autoregressive ordinal probit (AOP) model of M¨uller and Czado (2005) is utilized and fitted by a grouped move multigrid Monte Carlo (GM-MGMC) Gibbs sampler. Initially, covariates are selected using proportional odds models ignoring this dependency. Model fit and model comparison are discussed. The analysis shows that windchill and sunshine length, but not humidity and pressure differences have an effect in addition to a high dependence on previous measurements. A comparison with proportional odds specifications shows that the AOP models are preferred.

In this paper we investigate intraday data of the IBM stock and a time series representing the sleep states of a newborn child. In both cases we are interested in the influence of several covariates observed together with the response series. For this purpose we use on the one hand the regression model proposed in Müller and Czado (2002), on the other hand the ordered probit model. The parameters are estimated with the GM-MGMC algorithm described in Müller and Czado (2002). Predictions are computed to test the results.

In this paper binary state space mixed models of Czado and Song (2001) are applied to construct individual risk profiles based on a daily dairy of a migraine headache sufferer. These models allow for the modeling of a dynamic structure together with parametric covariate effects. Since the analysis is based on posterior inference using Markov Chain Monte Carlo (MCMC) methods, Bayesian model fit and model selection criteria are adapted to these binary state space mixed models. It is shown how they can be used to select an appropriate model, for which the probability of a headache today given the occurrence or nonoccurrence of a headache yesterday in dependency on weather conditions such as windchill and humidity can be estimated. This can provide the basis for pain management of such patients.

One important component of model selection using generalized linear models (GLM) is the choice of a link function. Approximate Bayes factors are used to assess the improvement in fit over a GLM with canonical link when a parametric link family is used. For this approximate Bayes factors are calculated using the approximations given in Raftery (1996), together with a reference set of prior distributions. This methodology can also be used to differentiate between different parametric link families, as well as allowing one to jointly select the link family and the independent variables. This involves comparing nonnested models. This is illustrated using parametric link families studied in Czado (1997) for two data sets involving binomial responses.