Testing parametric models in linear-directional regression
نویسندگان
چکیده
This paper presents a goodness-of-fit test for parametric regression models with scalar response and directional predictor, that is, vectors in a sphere of arbitrary dimension. The testing procedure is based on the weighted squared distance between a smooth and a parametric regression estimator, where the smooth regression estimator is obtained by a projected local approach. Asymptotic behavior of the test statistic under the null hypothesis and local alternatives is provided, jointly with a consistent bootstrap algorithm for application in practice. A simulation study illustrates the performance of the test in finite samples. The procedure is also applied to a real data example from text mining.
منابع مشابه
EVALUATION OF CONCRETE COMPRESSIVE STRENGTH USING ARTIFICIAL NEURAL NETWORK AND MULTIPLE LINEAR REGRESSION MODELS
In the present study, two different data-driven models, artificial neural network (ANN) and multiple linear regression (MLR) models, have been developed to predict the 28 days compressive strength of concrete. Seven different parameters namely 3/4 mm sand, 3/8 mm sand, cement content, gravel, maximums size of aggregate, fineness modulus, and water-cement ratio were considered as input variables...
متن کاملRegression Modeling for Spherical Data via Non-parametric and Least Square Methods
Introduction Statistical analysis of the data on the Earth's surface was a favorite subject among many researchers. Such data can be related to animal's migration from a region to another position. Then, statistical modeling of their paths helps biological researchers to predict their movements and estimate the areas that are most likely to constitute the presence of the animals. From a geome...
متن کاملOn describing multivariate skewed distribu- tions: A directional approach
Most multivariate measures of skewness in the literature measure the overall skewness of a distribution. These measures were designed for testing the hypothesis of distributional symmetry and their relevance for describing skewed distributions is less obvious. In this article, we consider the problem of characterising the skewness of multivariate distributions. We define directional skewness as...
متن کاملTesting Lack-of-fit of Parametric Regression Models Using Nonparametric Regression Techniques
Data-driven lack-of-fit tests are derived for parametric regression models using fit comparison statistics that are based on nonparametric linear smoothers. The tests are applicable to settings where the usual bandwidth/smoothing parameter asymptotics apply to the null model, which includes testing for nonlinear models and some linear models. Large sample distribution theory is established for ...
متن کاملBootstrap tests for the error distribution in linear and nonparametric regression models
In this paper we investigate several tests for the hypothesis of a parametric form of the error distribution in the common linear and nonparametric regression model, which are based on empirical processes of residuals. It is well known that tests in this context are not asymptotically distribution-free and the parametric bootstrap is applied to deal with this problem. The performance of the res...
متن کامل