Mikis D. Stasinopoulos

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BACKGROUND Secular trends in height and weight are reasonably well documented in Europe. Corresponding observations for skeletal maturation are lacking. AIM To assess secular trends in height, body mass and skeletal maturity of Portuguese children and adolescents and to provide updated reference values for skeletal maturity scores (SMSs). SUBJECTS AND(More)
OBJECTIVES To construct age- and gender-specific percentiles for gross motor coordination (MC) tests and to explore differences in gross MC in normal-weight, overweight and obese children. METHODS Data are from the "Healthy Growth of Madeira Study," a cross-sectional study carried out in children, aged 6-14 years. All 1,276 participants, 619 boys and 657(More)
OBJECTIVE To evaluate the effectiveness of a computer-generated tailored intervention leaflet compared with a generic leaflet aimed at increasing brown bread, wholegrain cereal, fruit and vegetable intakes in adolescent girls. DESIGN Clustered randomised controlled trial. Dietary intake was assessed via three 24 h dietary recalls. SETTING Eight(More)
Generalized Additive Models for Location, Scale and Shape (GAMLSS) were introduced by Rigby and Stasinopoulos (2005). GAMLSS is a general framework for univariate regression type statistical problems. In GAMLSS the exponential family distribution assumption used in Generalized Linear Model (GLM) and Generalized Additive Model (GAM), (see Nelder and(More)
This paper introduces two general models for computing centiles when the response variable Y can take values between 0 and 1, inclusive of 0 or 1. The models developed are more flexible alternatives to the beta inflated distribution. The first proposed model employs a flexible four parameter logit skew Student t (logitSST) distribution to model the response(More)
In this paper we h a ve proposed a class of Generalized Autoregressive M o v-ing Average GARMA models which extend univariate ARMA models to a non-Gaussian situation i.e. they extend the univariate Generalized Linear Model to incorporate time dependence in the observations. The simplicity of the tting algorithm within the iteratively reweighted least(More)