Bayesian spatial monotonic multiple regression
نویسندگان
چکیده
منابع مشابه
Bayesian Spatial Quantile Regression
Statistical Science) Bayesian Spatial Quantile Regression by Kristian Lum Department of Statistical Science Duke University
متن کاملBayesian Spatial Quantile Regression.
Tropospheric ozone is one of the six criteria pollutants regulated by the United States Environmental Protection Agency under the Clean Air Act and has been linked with several adverse health effects, including mortality. Due to the strong dependence on weather conditions, ozone may be sensitive to climate change and there is great interest in studying the potential effect of climate change on ...
متن کاملBayesian Wavelet Regression for Spatial Estimation
We consider the problem of estimating the properties of an oil reservoir, like porosity and sand thickness, in an exploration scenario where only a few wells have been drilled. We use gamma ray records measured directly from the wells as well as seismic traces recorded around the wells. To model the association between the soil properties and the signals, we fit a linear regression model. Addit...
متن کاملGeneralized smooth monotonic regression
Common approaches to monotonic regression focus on the case of a unidimensional covariate and continuous dependent variable. Here a general approach is proposed that allows for additive and multiplicative structures where one or more variables have monotone influence on the dependent variable. In addition the approach allows for dependent variables from an exponential family, including binary a...
متن کاملNon-Monotonic Spatial Reasoning
The systematic modelling of dynamic spatial systems is a key requirement in a wide range of application areas such as commonsense cognitive robotics, computer-aided architecture design, dynamic geographic information systems. We present ASPMT(QS), a novel approach and fully-implemented prototype for non-monotonic spatial reasoning —a crucial requirement within dynamic spatial systems— based on ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Biometrika
سال: 2018
ISSN: 0006-3444,1464-3510
DOI: 10.1093/biomet/asy019