Effect of within-litter birth weight variation on piglet survival and pre-weaning weight gain in a commercial herd

There are several factors that affect piglet survival and this has a bearing on sow productivity. Ten variables that influence pre-weaning vitality were analysed using records from the Pig Industry Board, Zimbabwe. These included individual piglet birth weight, piglet origin (nursed in original litter or fostered), sex, relative birth weight expressed as standard deviation units, sow parity, total number of piglets born, year and month of farrowing, within-litter variability and the presence of stillborn or mummified littermates. The main factors that influenced piglet mortality were fostering, parity and within-litter variability especially the weight of the individual piglet relative to the average of the litter (P<0.05). Presence of a mummified or stillborn littermate, which could be a proxy for unfavourable uterine environment or trauma during the birth process, did not influence pre-weaning mortality. Variability within a litter and the deviation of the weight of an individual piglet from the litter mean, influenced survival to weaning. It is, therefore, advisable for breeders to include uniformity within the litter as a selection criterion. The recording of various variables by farmers seems to be a useful management practice to identify piglets at risk so as to establish palliative measures. Further, farmers should know which litters and which piglets within a litter are at risk and require more attention.

From Regression to Classification in Support Vector Machines

We study the relation between support vector machines (SVMs) for regression (SVMR) and SVM for classification (SVMC). We show that for a given SVMC solution there exists a SVMR solution which is equivalent for a certain choice of the parameters. In particular our result is that for $epsilon$ sufficiently close to one, the optimal hyperplane and threshold for the SVMC problem with regularization parameter C_c are equal to (1-epsilon)^{- 1} times the optimal hyperplane and threshold for SVMR with regularization parameter C_r = (1-epsilon)C_c. A direct consequence of this result is that SVMC can be seen as a special case of SVMR.

On the Noise Model of Support Vector Machine Regression

Support Vector Machines Regression (SVMR) is a regression technique which has been recently introduced by V. Vapnik and his collaborators (Vapnik, 1995; Vapnik, Golowich and Smola, 1996). In SVMR the goodness of fit is measured not by the usual quadratic loss function (the mean square error), but by a different loss function called Vapnik"s $epsilon$- insensitive loss function, which is similar to the "robust" loss functions introduced by Huber (Huber, 1981). The quadratic loss function is well justified under the assumption of Gaussian additive noise. However, the noise model underlying the choice of Vapnik's loss function is less clear. In this paper the use of Vapnik's loss function is shown to be equivalent to a model of additive and Gaussian noise, where the variance and mean of the Gaussian are random variables. The probability distributions for the variance and mean will be stated explicitly. While this work is presented in the framework of SVMR, it can be extended to justify non-quadratic loss functions in any Maximum Likelihood or Maximum A Posteriori approach. It applies not only to Vapnik's loss function, but to a much broader class of loss functions.

On the V(subscript gamma) Dimension for Regression in Reproducing Kernel Hilbert Spaces

This paper presents a computation of the $V_gamma$ dimension for regression in bounded subspaces of Reproducing Kernel Hilbert Spaces (RKHS) for the Support Vector Machine (SVM) regression $epsilon$-insensitive loss function, and general $L_p$ loss functions. Finiteness of the RV_gamma$ dimension is shown, which also proves uniform convergence in probability for regression machines in RKHS subspaces that use the $L_epsilon$ or general $L_p$ loss functions. This paper presenta a novel proof of this result also for the case that a bias is added to the functions in the RKHS.

Time-series regression models to study the short-term effects of environmental factors on health

Time series regression models are especially suitable in epidemiology for evaluating short-term effects of time-varying exposures on health. The problem is that potential for confounding in time series regression is very high. Thus, it is important that trend and seasonality are properly accounted for. Our paper reviews the statistical models commonly used in time-series regression methods, specially allowing for serial correlation, make them potentially useful for selected epidemiological purposes. In particular, we discuss the use of time-series regression for counts using a wide range Generalised Linear Models as well as Generalised Additive Models. In addition, recently critical points in using statistical software for GAM were stressed, and reanalyses of time series data on air pollution and health were performed in order to update already published. Applications are offered through an example on the relationship between asthma emergency admissions and photochemical air pollutants

Regression analysis of compositional data when both the dependent variable and independent variable are components

It is well known that regression analyses involving compositional data need special attention because the data are not of full rank. For a regression analysis where both the dependent and independent variable are components we propose a transformation of the components emphasizing their role as dependent and independent variables. A simple linear regression can be performed on the transformed components. The regression line can be depicted in a ternary diagram facilitating the interpretation of the analysis in terms of components. An exemple with time-budgets illustrates the method and the graphical features

An alternative method for dating unknown tephras based on a segmented regression model

In CoDaWork’05, we presented an application of discriminant function analysis (DFA) to 4 different compositional datasets and modelled the first canonical variable using a segmented regression model solely based on an observation about the scatter plots. In this paper, multiple linear regressions are applied to different datasets to confirm the validity of our proposed model. In addition to dating the unknown tephras by calibration as discussed previously, another method of mapping the unknown tephras into samples of the reference set or missing samples in between consecutive reference samples is proposed. The application of these methodologies is demonstrated with both simulated and real datasets. This new proposed methodology provides an alternative, more acceptable approach for geologists as their focus is on mapping the unknown tephra with relevant eruptive events rather than estimating the age of unknown tephra. Kew words: Tephrochronology; Segmented regression

Regression periods in infancy. A case study from Catalonia.

Based on Rijt-Plooij and Plooij’s (1992) research on emergence of regression periods in the first two years of life, the presence of such periods in a group of 18 babies (10 boys and 8 girls, aged between 3 weeks and 14 months) from a Catalonian population was analyzed. The measurements were a questionnaire filled in by the infants’ mothers, a semi-structured weekly tape-recorded interview, and observations in their homes. The procedure and the instruments used in the project follow those proposed by Rijt-Plooij and Plooij. Our results confirm the existence of the regression periods in the first year of children’s life. Inter-coder agreement for trained coders was 78.2% and within-coder agreement was 90.1 %. In the discussion, the possible meaning and relevance of regression periods in order to understand development from a psychobiological and social framework is commented upon

Factores pronósticos de cáncer de seno: Hospital Militar Central enero 2003 a diciembre 2008

Introducción: el riesgo de desarrollar cáncer de seno durante la vida es del 13,4% (1 de cada 7 mujeres) y la posibilidad de morir por la enfermedad después del diagnostico es cercana al 30%. Pacientes y Métodos: es un estudio de cohorte abierta retrospectiva en el que se analizó la sobrevida según los factores pronósticos de las pacientes con cáncer de seno del hospital militar central en el periodo de enero de 2003 a diciembre de 2008. Los factores pronósticos son: Edad, estadío del tumor al momento del diagnóstico, Grado de diferenciación del tumor, presencia de metástasis al momento del diagnóstico, presencia de metástasis, número de sitios de metástasis, erb2, presencia de ganglios afectados, número de ganglios positivos, receptores estrogénicos, receptores de progestágeno, tratamiento con trastuzumab, tratamiento con hormonoterapia; el análisis estadístico se realizó a partir de la herramienta de recolección de datos, esta base de datos fue trasladada al programa SPSS. Resultados: participaron 171 mujeres. La presencia de receptores para estrógenos positivos se correlaciona con una mayor sobrevida con una diferencia estadísticamente significativa (p=0.015). Durante el periodo de tiempo del estudio fallecieron 23 pacientes (13.4%), de las cuales 20 (86%) presentaban Carcinoma Canalicular Infiltrante y 21 (91%) presentaban estadios avanzados del carcinoma. Conclusiones: las características demográficas de nuestra población son similares a lo publicado en la literatura, sin variantes estadísticamente significativas frente a los hallazgos internacionales. En nuestro análisis hubo una fuerte correlación de la presencia de estrógenos positivos en relación al tiempo de sobrevida.

Use of multilevel logistic regression to identify the causes of differential item functioning

Resumen tomado de la publicaci??n

Regression-based techniques for statistical decision making in singlecase designs

Resumen tomado de la publicaci??n

Resolución espontánea de reflujo vesicoureteral primario en niños: factores predictores y nomograma de predicción

Introducción: Determinamos la proporción de resolución espontánea de RVU primario en una población de niños menores de 5 años así como los factores que influyen y predicen tal resolución, con base en lo cual diseñamos un nomograma que permite determinar la posibilidad de resolución espontánea de cada grado de reflujo a los 3 años de su diagnóstico Metodología: Incluimos 407 niños con diagnóstico de RVU primario en un periodo de 10 años. Mediante análisis de asociación y comparaciones de promedios se determinaron las variables que se comportaron como factores de riesgo para fallar en obtener resolución espontanea y por un modelo de regresión logística binomial se confirmaron asociaciones. Se practicaron comparaciones mediante ANOVA o t-test y así como análisis de sobrevida mediante Log Rank Test para determinar las variables que influían también en el tiempo necesario para obtener resolución espontánea. Resultados: Las tasas de resolución espontánea fueron 92%, 85%, 56.4%, 21% y 5% para los grados I a V de reflujo respectivamente. En el análisis multivariado, Las variables nefropatía por reflujo (sig=0,000), Síndrome de evacuación disfuncional (SED) (sig=0,000) y bilateralidad (sig=0,006) fueron los factores de riesgo independientes para la falla del RVU en resolver espontáneamente. Sin embargo, en los análisis de sobrevida solo la variable SED demostró influir en el tiempo necesario para obtener resolución espontanea (sig=0,002). Discusión: Los hallazgos de este estudio ratifican la importancia de incluir variables como SED, nefropatía por reflujo y lateralidad en los modelos de predicción de resolución espontánea del RVU.