given a mapping $f:xto x$ we naturally associate to it a monotonic map $gg_f:exp xto exp x$ from the power set of $x$ into itself, thus inducing a generalized topology on $x$. in this paper we investigate some properties of generalized topologies which are defined by such a procedure.

Projection pursuit regression (PPR) can be used to estimate a smooth function of several variables from noisy and scattered data. The estimate is a sum of smoothed one-dimensional projections of the variables. This paper discusses an extension of PPR to exponential family distributions, called generalized projection pursuit regression (GPPR). The proposed model allows multiple responses and non...

An on-going challenge for decision makers is the interpretation of temporal trends from monitoring data given that environmental processes often generate complex data that are multivariate and potentially nonlinear. Generalized additive models (GAMs) is a wellsuited modelling framework for uncovering such trends and unifying datasets. This approach allows flexible specification of regression sp...

A common goal of epidemiologic research is to study the association between a certain exposure and a certain outcome, while controlling for important covariates. This is often done by fitting a restricted mean model for the outcome, as in generalized linear models (GLMs) and in generalized estimating equations (GEEs). If the covariates are high-dimensional, then it may be difficult to well spec...

Randomized experiments have become increasingly important for political scientists and campaign professionals. With few exceptions, these experiments have addressed the overall causal effect of an intervention across the entire population, known as the average treatment effect (ATE). A much broader set of questions can often be addressed by allowing for heterogeneous treatment effects. We discu...

The generalized additive model is a well established and strong tool that allows to model smooth effects of predictors on the response. However, if the link function, which is typically chosen as the canonical link, is misspecified, substantial bias is to be expected. A procedure is proposed that simultaneously estimates the form of the link function and the unknown form of the predictor functi...

Generalized additive models (GAMs) have been widely used. While the procedure for fitting a generalized additive model to independent data has been well established, not as much work has been done when the data are correlated. The currently available methods are not completely satisfactory in practice. A new approach is proposed to fit generalized additive models with spatio-temporal data via t...

In this article, we study a partially linear single-index model for longitudinal data under a general framework which includes both the sparse and dense longitudinal data cases. A semiparametric estimation method based on the combination of the local linear smoothing and generalized estimation equations (GEE) is introduced to estimate the two parameter vectors as well as the unknown link functi...

In this current study a generalized super-efficiency model is first proposed for ranking extreme efficient decision making units (DMUs) in stochastic data envelopment analysis (DEA) and then, a deterministic (crisp) equivalent form of the stochastic generalized super-efficiency model is presented. It is shown that this deterministic model can be converted to a quadratic programming model. So fa...