Interval-valued data regression using partial linear model

ABSTRACT Semi-parametric modelling of interval-valued data is of great practical importance, as exampled by applications in economic and financial data analysis. We propose a flexible semi-parametric modelling of interval-valued data by integrating the partial linear regression model based on the Center & Range method, and investigate its estimation procedure. Furthermore, we introduce a test statistic that allows one to decide between a parametric linear model and a semi-parametric model, and approximate its null asymptotic distribution based on wild Bootstrap method to obtain the critical values. Extensive simulation studies are carried out to evaluate the performance of the proposed methodology and the new test. Moreover, several empirical data sets are analysed to document its practical applications.

[1]  T. Severini,et al.  Quasi-Likelihood Estimation in Semiparametric Models , 1994 .

[2]  Robert A. Lordo,et al.  Nonparametric and Semiparametric Models , 2005, Technometrics.

[3]  Francisco de A. T. de Carvalho,et al.  Centre and Range method for fitting a linear regression model to symbolic interval data , 2008, Comput. Stat. Data Anal..

[4]  Enno Mammen,et al.  Testing Parametric Versus Semiparametric Modelling in Generalized Linear Models , 1996 .

[5]  A. Seheult,et al.  Analysis of Field Experiments by Least Squares Smoothing , 1985 .

[6]  C. F. Wu JACKKNIFE , BOOTSTRAP AND OTHER RESAMPLING METHODS IN REGRESSION ANALYSIS ' BY , 2008 .

[7]  Lynne Billard,et al.  Dependencies and Variation Components of Symbolic Interval-Valued Data , 2007 .

[8]  Clive W. J. Granger,et al.  Semiparametric estimates of the relation between weather and electricity sales , 1986 .

[9]  P. Robinson ROOT-N-CONSISTENT SEMIPARAMETRIC REGRESSION , 1988 .

[10]  Yongho Jeon,et al.  A Nonparametric Kernel Approach to Interval-Valued Data Analysis , 2015, Technometrics.

[11]  L. Billard,et al.  Regression Analysis for Interval-Valued Data , 2000 .

[12]  Renata M. C. R. de Souza,et al.  Interval kernel regression , 2014, Neurocomputing.

[13]  Grace Wahba,et al.  Partial Spline Models for the Inclusion of Tropopause and Frontal Boundary Information in Otherwise Smooth Two- and Three-Dimensional Objective Analysis , 1986 .

[14]  R. Tibshirani,et al.  Generalized Additive Models , 1986 .

[15]  Francisco de A. T. de Carvalho,et al.  Constrained linear regression models for symbolic interval-valued variables , 2010, Comput. Stat. Data Anal..

[16]  Yan Sun Linear regression with interval‐valued data , 2016 .

[17]  Wolfgang Härdle,et al.  Partially Linear Models , 2000 .

[18]  G. Cordeiro,et al.  Bivariate symbolic regression models for interval-valued variables , 2011 .

[19]  Changwon Lim,et al.  Interval-valued data regression using nonparametric additive models , 2016 .

[20]  Francisco de A. T. de Carvalho,et al.  Forecasting models for interval-valued time series , 2008, Neurocomputing.

[21]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[22]  Eufrásio de Andrade Lima Neto,et al.  Regression model for interval-valued variables based on copulas , 2015 .

[23]  Junjie Wu,et al.  CIPCA: Complete-Information-based Principal Component Analysis for interval-valued data , 2012, Neurocomputing.

[24]  K. Singh,et al.  Discussion: Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis , 1986 .

[25]  P. Speckman Kernel smoothing in partial linear models , 1988 .

[26]  L. Billard,et al.  Symbolic Regression Analysis , 2002 .

[27]  M. Negash,et al.  Regression Analysis for Interval-Valued Data , 2018 .

[28]  Emmanuel Flachaire,et al.  The wild bootstrap, tamed at last , 2001 .

[29]  Monique Noirhomme-Fraiture,et al.  Symbolic Data Analysis and the SODAS Software , 2008 .

[30]  Junjie Wu,et al.  Linear regression of interval-valued data based on complete information in hypercubes , 2012 .