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- Haptoglobin, acid phosphatase and demographic factors: obesity riskPublication . Ramos, Maria do Rosário; Carolino, Elisabete; Oliveira, Teresa; Silva, A. P.; Carvalho, R.; Bicho, M.The aim of this work is to study the risk of obesity posed by two genetic factors: haptoglobin phenotype and acid phosphatase phenotype, one enzymatic activity: acid phosphatase activity (ACP1), age and gender. Haptoglobin (Hp) is a protein of the immune system, and three phenotypes of Hp are found in humans: Hp1-1, Hp2-1, and Hp2-2. This protein is associated with a susceptibility to common pathological conditions, such as obesity. ACP1 is an intracellular enzyme The phenotypes of ACP1 (AA, AB, AC, BB, BC, CC) are also considered. We took a sample of 127 subjects with complete data from 714 registers. Since we intend to identify risk factors for obesity, an ordinal regression model is adjusted, using the Body Mass Index, BMI, to define weight categories. Haptoglobin phenotype, enzymatic activity of ACP1, acid phosphatase phenotype, age and gender are considered as regressor variables. We found three factors associated with an increased risk of obesity: phenotype Hp2-1 of haptoglobin (estimated odds ratio OR 11.54), phenotype AA of acid phosphatase (OR 33.788) and age (OR 1.39). The interaction between phenotype Hp2-1 and phenotype AC is associated with a decreased risk of obesity (OR 0.032); The interaction between phenotype AA and ACP1 activity is associated with a decreased risk of obesity (OR 0.954).
- Joint regression analysis applied to genotype stability evaluation over yearsPublication . Oliveira, Amilcar; Oliveira, Teresa; Mejza, Stanislaw; Mexia, João TiagoMost genotype differences connected with yield stability are due to genotype environment interaction. The presence and dimension of this interaction are the factors that determine the performance of genotypes in distinct environments. The environmental factors, like annual rainfall, temperature, diseases or soil fertility, can only explain part of this interaction. Many statistical tools have been developed with the aim to explain the information contained in the GE interaction data matrix. In our work we use the Joint Regression Analysis (JRA), the Zig-Zag Algorithm to estimate the regression coefficients and the multiple comparison tests of Scheffé, Tukey and Bonferroni. We point out not just the limitations of the JRA when used year by year, but also genotype selection advantage from general JRA over years. Data of the Portuguese Plant Breeding Board were used to carry the year and over years analyses of yielding stability of 22 different genotypes of oat (Avena sativa L.) at six different locations in the years 2002, 2003 and 2004.
- Estatística aplicadaPublication . Oliveira, Teresa
- Multiple regression models for lactation curvesPublication . Pereira, Marta S. P.; Oliveira, Teresa; Mexia, João TiagoSeveral methods have been developed in order to study lactation curves. However, the lactation curves are often not well adjusted since several factors affect milk production. The usual model used to describe a lactation curve is Wood’s Model, which generally uses a logarithmic transformation of an incomplete gamma curve to obtain least squares estimates of three constants: a - a scaling factor associated with average daily yield; b - associated with prepeak curvature; and c associated with post-peak curvature (Wood, 1976). Some disadvantages of Wood’s model are strongly connected with the overestimation of milk production at the beginning of lactation, with underestimation of the lactation peak: the self correlated residuals and highly correlated parameter estimates (Scott et al,1996). Fleischmann’s Method is usually used to estimate total milk production. This method generally overestimates actual yields up to peak lactation as well as yield during the period following the last measurement, but underestimates yields for other periods (Norman et al, 1999). The total milk yield estimate according to this method, considers a constant daily milk production between two records and equal to the mean of these two records, which does not describe the true variation of milk secretion during lactation. The mentioned disadvantages led us to consider the milk curve concept as a graphical representation of milk production described by mathematical models. In our work we considered a new approach using polynomial regression, one for each group. Polynomial curves were adjusted to daily milk records for each group and the respective hypo-graphic area was calculated to estimate total yields. An ANOVA to the comparison of these total yiels was carried out and the Scheffémultiple comparison method was applied. This approach greatly increases the power of the test, enabling work with smaller experiments, the reason for this increase being the replacement of classical replicates by time replicates, leading to a great increase in the degrees of freedom. Another advantage of this method is the use of a continuous process instead of an obligatory discrete process conversion. Differences between protein supplements and stocking rate were found using an adaptation of Scheffé's method. We concluded that a lower stocking rate and high protein content in supplement enable higher milk production.
- Hierarchical linear models in education sciences: an applicationPublication . Valente, Vítor; Oliveira, TeresaThe importance of hierarchical structured data analysis, based on appropriate statistical models, is very well known in several research areas. In this paper we describe an application in Education Sciences: we have students grouped in classes belonging to schools, which in turn are scattered throughout the country. This grouped organization is labelled as a hierarchical or multilevel structure, and the models usually adopted for statistical analysis of this kind of data are hierarchical linear or multilevel models. The development of these models takes into account data variability within and among the hierarchical levels. We apply a hierarchical linear model (HLM) with two levels – students and schools – in order to identify relevant differences in student performance (10th grade high school in 2004/2005), considering three scientific subjects and comparing two different regions of Portugal: Coastal and Inland.
- Analysis of residuals and adjustment in JRAPublication . Oliveira, Amilcar; Oliveira, Teresa; Mexia, João TiagoJoint Regression Analysis (JRA) is based in linear regression applied to yields, adjusting one linear regression per cultivar. The environmental indexes in JRA correspond to a non observable regressor which measures the productivity of the blocks in the field trials. Usually zig-zag algorithm is used in the adjustment. In this algorithm, minimizations for the regression coefficients alternate with those for the environmental indexes. The algorithm has performed very nicely but a general proof of convergence to the absolute minimum of the sum of squares of residues is still lucking. We now present a model for the residues that may be used to validate the adjustments carried out by the zig-zag algorithm.
- Distribution view: a tool to write and simulate distributionsPublication . Coelho, José; Branco, Fernando; Oliveira, TeresaIn our work we present a tool to write and simulate distributions. This tool allows to write mathematical expressions which can contain not only functions and variables, but also statistical distributions, including mixtures. Each time the expression is evaluated, for all inner distributions, is generated a value according to the distribution and is used for expression value determination. The inversion method can be used in this language, allowing to generate all distributions that have an expression for cdf inverse. The variables in the language allow the generation of several correlated distributions. To illustrate the advantages of using distribution view we present two applications: One in Project Risk Management, compares the PERT method with Simulation alternative; The other in Statistics, compares the Power of Randomization Test with the power of Student-t Test , using the set of Marron-Wand distributions.