title: Self-modeling for two-dimensional response curves.
abstract: Two-dimensional response curves are an important experimental outcome in speech kinematics and other areas of research. These parameterized curves are usually obtained by recording the two-dimensional location of an object over time. In this setting, time is the independent variable and the x and y locations on specified coordinate axes define the multivariate response. Collections of such parameterized curves can be obtained either from one subject or from a number of different subjects, each producing one or several realizations of the response curve. When only one dependent variable is observed over time and no parametric model is specified, self-modeling regression (SEMOR) is an attractive modeling approach. SEMOR assumes that each of a collection of curves differs from a smooth, average shape function by some simple parametric transformation of the coordinate axes (usually linear). We will describe the extension of SEMOR to two-dimensional parameterized curves using affine transformations of a two-dimensional, time-parameterized shape function.
authors.affiliation: Spotfire, Inc., Cambridge, Massachusetts, USA.
authors.forename: W M
authors.initials: WM
authors.lastname: Ladd
authors.forename: M J
authors.initials: MJ
authors.lastname: Lindstrom
mesh_terms: D001699:Biometry; D006801:Humans; D015233:Models, Statistical; D009068:Movement / Q000502:physiology; D012044:Regression Analysis; D013060:Speech / Q000502:physiology*