type="main" xml:id="sjos12054-abs-0001"> <title type="main">ABSTRACT</title>The mode of a distribution provides an important summary of data and is often estimated on the basis of some non-parametric kernel density estimator. This article develops a new data analysis tool called modal linear regression in order to explore high-dimensional data. Modal linear regression models the conditional mode of a response Y given a set of predictors <b>x</b> as a linear function of <b>x</b>. Modal linear regression differs from standard linear regression in that standard linear regression models the conditional mean (as opposed to mode) of Y as a linear function of <b>x</b>. We propose an expectation–maximization algorithm in order to estimate the regression coefficients of modal linear regression. We also provide asymptotic properties for the proposed estimator without the symmetric assumption of the error density. Our empirical studies with simulated data and real data demonstrate that the proposed modal regression gives shorter predictive intervals than mean linear regression, median linear regression and MM-estimators.
