... In this paper, we introduce nonparametric estimation for the cumulative incidences of competing risks when the incidence time is doubly truncated. Two different estimators are proposed depending on whether ...
This paper focuses on the problem of testing the null hypothesis that the variance functions of two populations are equal versus one-sided alternatives under a general nonparametric heteroscedastic regression ...
Survival analysis includes a wide variety of methods for analyzing time‐to‐event data. One basic but important goal in survival analysis is the comparison of survival curves between groups. Several nonparametric ...
Several aquatic living resources are overexploited. An example of this is a barnacle fishery, which is a commercial species that commands high prices in the market. We feel that not enough is known about ...
The problem of testing for the parametric form of the conditional variance is considered
in a fully nonparametric regression model. A test statistic based on a weighted
L2-distance between the empirical ...
A test for the equality of error distributions in two nonparametric regression models is proposed. The test statistic is based on comparing the empirical characteristic functions of the residuals calculated ...
... which are treated as censored observations. However, the latter seems to perform adequately well in our simulation study. Nonparametric bootstrap was consistently used throughout, while other bootstrap ...
Accurate diagnosis of disease is of fundamental importance in clinical practice and medical research. Before a medical diagnostic test is routinely used in practice, its ability to distinguish between ...
... there are only three R packages providing receiver operating-characteristic curve confidence regions: plotROC, pROC and fbroc. This work tries to fill this gap studying and proposing a new nonparametric ...
The purpose of this paper is two fold. First, we investigate estimation for varying coefficient partially linear models in which covariates in the nonparametric part are measured with errors. As there ...
This article proposes a new general methodology for constructing nonparametric and semiparametric Asymptotically Distribution-Free (ADF) tests for semiparametric hypotheses in regression models for possibly ...
... we focus on an estimator for the covariate-specific receiver-operating characteristic curve based on direct regression modelling and nonparametric smoothing techniques. This approach defines the class ...
Several procedures have been proposed for testing goodness-of-fit to the error distribution in nonparametric regression models. The null distribution of the associated test statistics is usually approximated ...
The NPMLE of a distribution function from doubly truncated data was introduced in the seminal paper of Efron and Petrosian. The consistency of the Efron-Petrosian estimator depends however on the assumption ...
Nonparametric estimation of the transition probability matrix of a progressive multi-state model is consideredunder cross-sectional sampling. Two different estimators adapted to possibly right-censored ...
... and imputation methods, a three-stage approach is proposed to construct the estimators of the linear part and the nonparametric varying-coefficient function for this model. At the same time, we discuss ...
... wavelengths (optimum wavelengths) from the continuous spectrum, which simplifies the regression problem. Finally, it compares the results to those obtained by means of two different methods: a nonparametric ...
Several procedures have been proposed for testing the equality of error distributions in two or more nonparametric regression models. Here we deal with methods based on comparing estimators of the cumulative ...
We present the R npregfast package via some applications involved with the study of living organisms. The package implements nonparametric estimation procedures in regression models with or without factor-by-curve ...
One major goal in clinical applications of time-to-event data is the estimation of survival with censored data. The usual nonparametric estimator of the survival function is the time-honored Kaplan-Meier ...
Survival analysis has become in a common procedure in biomedical researches. Conventionally, the well-known nonparametric Kaplan-Meier (KM) estimator is used in order to approximate the real survivor curve. ...
... procedure to select the best model in different regression frameworks (parametric or nonparametric). The developed methodology, which can be equally applied to linear models, generalized linear models ...
For the high-dimensional partially linear varying coefficient models where covariates in the nonparametric part are measured with additive errors, we, in this paper, study asymptotic distributions of a ...
... In this paper, attention is focused on an estimator for the covariate-specific ROC curve based on direct regression modelling and nonparametric smoothing techniques. This approach defines the class of generalized ...
... the estimation of the survival function conditional to a previous event. Different nonparametric approaches will be considered for estimating these quantities, all based on the Kaplan–Meier estimator of ...
... this work, we present new nonparametric estimators of the cumulative/dynamic time-dependent ROC curve that allow accounting for the possible modifying effect of current or past covariate measures on the ...
The problem of testing the null hypothesis that the regression functions of two populations are equal versus one-sided alternatives under a general nonparametric homoscedastic regression model is considered. ...
This paper analyses the behaviour of the goodness-of-fit tests for regression models. To this end, it uses statistics based on an estimation of the integrated regression function with missing observations ...
Nonparametric regression with a doubly truncated response is introduced. Local constant and local linear kernel-type estimators are proposed. Asymptotic expressions for the bias and the variance of the ...
One important goal in multi-state modeling is the estimation of transition probabilities. In longitudinal medical studies these quantities are particularly of interest since they allow for long-term predictions ...
On the basis of the idea of the Nadaraya–Watson (NW) kernel smoother and the technique of the local linear (LL) smoother, we construct the NW and LL estimators of conditional mean functions and their derivatives ...
Multi-state models are often used for modeling complex event history data. In these models the estimation of the transition probabilities is of particular interest, since they allow for long-term predictions ...
One major goal in clinical applications of multi-state models is the estimation of transition probabilities. The usual nonparametric estimator of the transition matrix for non-homogeneous Markov processes ...
In this paper we are interested in checking whether the conditional variances are equal in k >= 2 location-scale regression models. Our procedure is fully nonparametric and is based on the comparison of ...
This article studies a new procedure to test for the equality of k regression curves in a fully non-parametric context. The test is based on the comparison of empirical estimators of the characteristic ...
... specification of the distribution function of the truncation times. This semiparametric estimator outperforms the nonparametric maximum likelihood estimator when the parametric information is correct, ...
One major goal in clinical applications of multi-state models is the estimation of transition probabilities. The usual nonparametric estimator of the transition matrix for non-homogeneous Markov processes ...
... and studies a feasible estimation method for the transition probabilities in a progressive three-state model. We assume that the vector of gap times satisfies a nonparametric location-scale regression ...
... two models: a classic allometric model and a nonparametric regression model based on local linear kernel smoothers. Additionally, first derivatives were used to estimate a minimum capture size for this ...
... before the main event of interest. Nonparametric estimation of transition probabilities in this and other multistate models is usually performed through the Aalen–Johansen estimator under a Markov assumption. ...
... using a nonparametric regression method that permitted the construction of a continuous smooth surface. Asymmetries in cross-sections of the surface were identified using a bootstrap resampling technique ...