Robust distributed lag models using data adaptive shrinkage

Distributed lag models for hydrological data.

The distributed lag model (DLM), used most prominently in air pollution studies, finds application wherever the effect of a covariate is delayed and distributed through time. We specify modified formulations of DLMs to provide computationally attractive, flexible varying-coefficient models that are applicable in any setting in which lagged covariates are regressed on a time-dependent response. ...

Distributed lag non-linear models

Environmental stressors often show effects that are delayed in time, requiring the use of statistical models that are flexible enough to describe the additional time dimension of the exposure-response relationship. Here we develop the family of distributed lag non-linear models (DLNM), a modelling framework that can simultaneously represent non-linear exposure-response dependencies and delayed ...

Adaptive Spatial Indexing for Robust Distributed Data

A spatial index suitable for implementation of a multidimensionally keyed database (such as text retrieval system) in an unreliable, decentralized, distributed environment is shown to have complexity comparable to the Internet's Domain Name Service and better than USENET or Web search engines. The index is a graph mapped into Euclidean space with high smoothness, a property allowing efficient b...

Robust High - Dimensional Data Analysis Using a Weight Shrinkage

Factor Augmented Autoregressive Distributed Lag Models

This paper proposes a factor augmented autoregressive distributed lag (FADL) framework for analyzing the dynamic effects of common and idiosyncratic shocks. We first estimate the common shocks from a large panel of data with a strong factor structure. Impulse responses are then obtained from an autoregression, augmented with a distributed lag of the estimated common shocks. The approach has thr...