Bootstrapping for penalized spline regression.

We describe and contrast several different bootstrapping procedures for penalized spline smoothers. The bootstrapping procedures considered are variations on existing methods, developed under two different probabilistic frameworks. Under the first framework, penalized spline regression is considered an estimation technique to find an unknown smooth function. The smooth function is represented in a high dimensional spline basis, with spline coefficients estimated in a penalized form. Under the second framework, the unknown function is treated as a realization of a set of random spline coefficients, which are then predicted in a linear mixed model. We describe how bootstrapping methods can be implemented under both frameworks, and we show in theory and through simulations and examples that bootstrapping provides valid inference in both cases. We compare the inference obtained under both frameworks, and conclude that the latter generally produces better results than the former. The bootstrapping ideas are extended to hypothesis testing, where parametric components in a model are tested against nonparametric alternatives.Methods; Framework; Regression; Linear mixed model; Mixed model; Model; Theory; Simulation; Hypothesis testing;

Advances in prevention and therapy of neonatal dairy calf diarrhoea : a systematical review with emphasis on colostrum management and fluid therapy

Neonatal calf diarrhoea remains the most common cause of morbidity and mortality in preweaned dairy calves worldwide. This complex disease can be triggered by both infectious and non-infectious causes. The four most important enteropathogens leading to neonatal dairy calf diarrhoea are Escherichia coli, rota-and coronavirus, and Cryptosporidium parvum. Besides treating diarrhoeic neonatal dairy calves, the veterinarian is the most obvious person to advise the dairy farmer on prevention and treatment of this disease. This review deals with prevention and treatment of neonatal dairy calf diarrhoea focusing on the importance of a good colostrum management and a correct fluid therapy

Internal validity of a household food security scale is consistent among diverse populations participating in a food supplement program in Colombia

Objective: We assessed the validity of a locally adapted Colombian Household Food Security Scale (CHFSS) used as a part of the 2006 evaluation of the food supplement component of the Plan for Improving Food and Nutrition in Antioquia, Colombia (MANA – Plan Departamental de Seguridad Alimentaria y Nutricional de Antioquia). Methods: Subjects included low-income families with pre-school age children in MANA that responded affirmatively to at least one CHFSS item (n = 1,319). Rasch Modeling was used to evaluate the psychometric characteristics of the items through measure and INFIT values. Differences in CHFSS performance were assessed by area of residency, socioeconomic status and number of children enrolled in MANA. Unidimensionality of a scale by group was further assessed using Differential Item Functioning (DIF). Results: Most CHFSS items presented good fitness with most INFIT values within the adequate range of 0.8 to 1.2. Consistency in item measure values between groups was found for all but two items in the comparison by area of residency. Only two adult items exhibited DIF between urban and rural households. Conclusion: The results indicate that the adapted CHFSS is a valid tool to assess the household food security of participants in food assistance programs like MANA

Bootstrapping for penalized spline regression

We describe and contrast several different bootstrapping procedures for penalized spline smoothers. The bootstrapping procedures considered are variations on existing methods, developed under two different probabilistic frameworks. Under the first framework, penalized spline regression is considered an estimation technique to find an unknown smooth function. The smooth function is represented in a high dimensional spline basis, with spline coefficients estimated in a penalized form. Under the second framework, the unknown function is treated as a realization of a set of random spline coefficients, which are then predicted in a linear mixed model. We describe how bootstrapping methods can be implemented under both frameworks, and we show in theory and through simulations and examples that bootstrapping provides valid inference in both cases. We compare the inference obtained under both frameworks, and conclude that the latter generally produces better results than the former. The bootstrapping ideas are extended to hypothesis testing, where parametric components in a model are tested against nonparametric alternatives.status: publishe

Variable selection for additive partially linear models with measurement error

Backfitting technique, SCAD, Additive model, Local linear method, Measurement error,