Growth curves of sorghum roots via quantile regression with P-splines

Plant roots are a major pool of total carbon in the planet and their dynamics are directly relevant to greenhouse gas balance. Composted wastes are increasingly used in agriculture for environmental and economic reasons and their role as a substitute for traditional fertilizers needs to be tested on all plant components. Here we propose a regression quantile approach based on P-splines to assess, quantify and compare the root growth patterns in two treatment groups respectively undergoing compost and traditional fertilization

Cardiovascular dysfunction and vitamin D status in childhood acute lymphoblastic leukemia survivors

Vitamin D (25-OHD) has a role in bone health after treatment for cancer. 25-OHD deficiency has been associated with risk factors for cardiovascular disease, but no data focusing on this topic in childhood cancer survivors have been published. We investigated the 25-OHD status in children treated for acute lymphoblastic leukemia (ALL), and evaluated its influence on vascular function

Inferential tools in penalized logistic regression for small and sparse data: A comparative study

Abstract This paper focuses on inferential tools in the logistic regression model fitted by the Firth penalized likelihood. In this context, the Likelihood Ratio statistic is often reported to be the preferred choice as compared to the ‘traditional’ Wald statistic. In this work, we consider and discuss a wider range of test statistics, including the robust Wald, the Score, and the recently proposed Gradient statistic. We compare all these asymptotically equivalent statistics in terms of interval estimation and hypothesis testing via simulation experiments and analyses of two real datasets. We find out that the Likelihood ratio statistic does not appear the best inferential device in the Firth penalized logistic regressio

segmented: An R package to Fit Regression Models with Broken-Line Relationships

Segmented or broken-line models are regression models where the relationships between the response and one or more explanatory variables are piecewise linear, namely represented by two or more straight lines connected at unknown values: these values are usually referred as breakpoints, changepoints or even joinpoints

Smoothed score confidence interval for the breakpoint in segmented regression

For the breakpoint parameter in segmented regression we consider confidence intervals based on the score statistic. Due to unsmoothness of the score, we propose to build the confidence intervals using its smoothed version under proper shape restrictions. Some simulations are presented to assess the finite sample performance of the proposed approach

Bivariate Distributed Lag Models for the analysis of temperature-by-pollutant interaction effect on mortality.

This paper introduces Bivariate Distributed Lags Models (BDLMs) to investigate synergic effect of temperature and airborne particles on mortality. These models seem particulary attractive since they allow to model interactions between such environmental variables accounting for possible delayed effects. A B-spline framework is used to approximate the coefficient surface of the temperature-by-pollutant interaction and possible alternatives are also discussed. A case study of mortality time-series data in Palermo, Italy, is presented to illustrate the model

Analyzing temperature effects on mortality within the R environment: the constrained segmented distributed lag parameterization

Here we present and discuss the R package modTempEff including a set of functions aimed at modelling temperature effects on mortality with time series data. The functions fit a particular log linear model which allows to capture the two main features of mortality-temperature relationships: nonlinearity and distributed lag effect. Penalized splines and segmented regression constitute the core of the modelling framework. We briefly review the model and illustrate the functions throughout a simulated dataset

LASSO regression via smooth L1-norm approximation

This paper discusses estimation of regression model with LASSO penalty when the L1-norm is replaced with its parametric smooth approximation. The resulting parameter estimators are more manageable than those from standard LASSO, standard errors are easy computed via a sandwich formula, and the model degrees of freedom may be computed straightforwardly. Moreover the resulting objective function may be minimized using usual optimization algorithms for regular models, for instance Newton-Raphson or iterative least squares

Testing with a nuisance parameter present only under the alternative: a score-based approach with application to segmented modelling

We introduce a score-type statistic to test for a non-zero regression coefficient when the relevant term involves a nuisance parameter present only under the alternative. Despite the non-regularity and complexity of the problem and unlike the previous approaches, the proposed test statistic does not require the nuisance to be estimated. It is simple to implement by relying on the conventional distributions, such as Normal or t, and it justified in the setting of probabilistic coherence. We focus on testing for the existence of a breakpoint in segmented regression, and illustrate the methodology with an analysis on data of DNA copy number aberrations and gene expression profiles from 97 breast cancer patients; moreover some simulations reveal that the proposed test is more powerful than its competitors previously discussed in literature