In this article, we develop a nonparametric graphical model for multivariate random functions. Most existing models are restricted by the assumptions of Gaussian or copula distributions, which also imply linear relations among variables functions on different nodes. We relax those building our based new statistical object—the functional additive regression operator. By carrying out and neighbor...

We describe a method for removing the effect of confounders to reconstruct a latent quantity of interest. The method, referred to as "half-sibling regression," is inspired by recent work in causal inference using additive noise models. We provide a theoretical justification, discussing both independent and identically distributed as well as time series data, respectively, and illustrate the pot...

Tobler (1965) introduced bidimensional regression to the research field of geography in 1965 to provide a method for estimating mapping relations between two planes on the basis of regression modeling. The bidimensional regression method has been widely used within geographical research. However, the applicability in assessing the degree of similarity of two-dimensional patterns has not much ex...

We consider concepts and models for measuring inequality in the distribution of resources with a focus on how inequality varies as a function of covariates. Lorenz introduced a device for measuring inequality in the distribution of income that indicates how much the incomes below the u quantile fall short of the egalitarian situation where everyone has the same income. Gini introduced a summary...

In a hierarchical or fixed-order regression analysis, the independent variables are entered into the regression equation in a prespecified order. Such an analysis is often performed when the extra amount of variance accounted for in a dependent variable by a specific independent variable is the main focus of interest (e.g., Cohen & Cohen, 1983). For example, in the area of reading achievement, ...

Parameter estimation of the spatial auto-regression model (SAR) is important because we can model the spatial dependency, i.e., spatial autocorrelation present in the geo-spatial data. SAR is a popular data mining technique used in many geo-spatial application domains such as regional economics, ecology, environmental management, public safety, public health, transportation, and business. Howev...

Predictive modeling includes regression, both logistic and linear, depending upon the type of outcome variable. However, as the datasets are generally too large for a p-value to have meaning, predictive modeling uses other measures of model fit. Generally, too, there are enough observations so that the data can be partitioned into two or more datasets. The first subset is used to define (or tra...

Response modeling, which predicts whether each customer will respond or how much each customer will spend based on the database of customers, becomes a key factor of direct marketing. In previous researches, several classification approaches, include Support Vector Machines (SVM) and Neural Networks (NN), have been applied for response modeling. However, there are two drawbacks of conventional ...

A statistical model is described for the prediction of reaching motions using motion capture data on a variety of individuals performing reaches to a range of targets. The modeling approach allows for various inputs such as the stature, age and the location of the target to be specified and then computes the predicted trajectories of the kinematic chains of body markers necessary to place an ob...