Circular piecewise regression with applications to cell‐cycle data

Introduction to circular data

In many diverse scientific fields, the measurements are directions. For instance, a biologist may be measuring the direction of flight of a bird or the orientation of an animal. A series of such observations is called ”directional data”. Since a direction has no magnitude, these can be conveniently represented as points on the circumference of a unit circle centered at the origin or as unit ...

Web - based Supplementary Materials for “ Circular Piecewise Regression with an Application to Cell - cycle Biology

Extension of Logic regression to Longitudinal data: Transition Logic Regression

Logic regression is a generalized regression and classification method that is able to make Boolean combinations as new predictive variables from the original binary variables. Logic regression was introduced for case control or cohort study with independent observations. Although in various studies, correlated observations occur due to different reasons, logic regression have not been studi...

Inverse circular-circular regression

The problem of determining the values of the independent variable given a value of the dependent variable is commonly referred to as the inverse regression problem. This problem is also encountered in real life with circular data and we refer to it in that context as the inverse circular regression problem. For such a problem, we develop distance-basedmethods, and parametric methods, where we u...

Regression Methods for Pairwise Comparisons Data with Applications to Forestry

We present a regression formulation of the analytic hierarchy process (AHP) introduced by Saaty (1977) as a method of deriving a ratio scale of preferences (or priorities) concerning a set of m entities or attributes. This method involves a quantification of all m(m-1)/2 pairwise comparisons between the entities. The ratio scale is derived using an eigenvalue calculation on a matrix formed from...