東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Considering Uncertainty in Spatial M...

Rischard, Maxime.

Linked to FindBook

Google Book

Amazon

博客來

Considering Uncertainty in Spatial Models: Causal Inference, Missing Data Imputations, and Model Comparison.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Considering Uncertainty in Spatial Models: Causal Inference, Missing Data Imputations, and Model Comparison./
Author:	Rischard, Maxime.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	182 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-06, Section: B.
Contained By:	Dissertations Abstracts International82-06B.
Subject:	Statistics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28236240
ISBN:	9798684634536

Considering Uncertainty in Spatial Models: Causal Inference, Missing Data Imputations, and Model Comparison.
Rischard, Maxime.

Considering Uncertainty in Spatial Models: Causal Inference, Missing Data Imputations, and Model Comparison. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 182 p.

Source: Dissertations Abstracts International, Volume: 82-06, Section: B.

Thesis (Ph.D.)--Harvard University, 2019.

This item must not be sold to any third party vendors.

I present two applications of spatial and spatiotemporal models that stretch the traditional domain of these models, and a method for estimating the marginal likelihood in Bayesian models. Chapter 1 presents a spatial model for causal inference, in the setting of geographic regression discontinuity designs. The model is used to derive estimators of the treatment effect along a geographical border, to develop sensible estimands and estimators of the average treatment effect that account for the topology of the border, and to derive valid hypothesis tests. Chapter 2 addresses a bias in historical records of daily temperature minima and maxima due to the time of day at which the measurements are made, by deploying a spatiotemporal model in a missing data imputation framework. This is enabled by a novel and easy to implement Markov Chain Monte Carlo (MCMC) technique to sample the imputations conditionally on the observed daily extrema. Chapter 3 proposes a method for unbiasedly estimating the log ratio of normalizing constants of two unnormalized distributions, which can be used to estimate the marginal log likelihood of Bayesian models and for Bayesian cross-validation. These quantities are generally challenging to approximate from samples of the posterior, but are important for Bayesian model comparison and selection. I demonstrate the method on a spatial log Gaussian Cox process model and on a high-dimensional Bayesian variable selection model, and discuss how the lack of bias unlocks parallelization on multiple computing units.

ISBN: 9798684634536Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

MCMC

Considering Uncertainty in Spatial Models: Causal Inference, Missing Data Imputations, and Model Comparison.
LDR:02820nmm a2200397 4500 001 2280436
005 20210827095949.5
008 220723s2019 ||||||||||||||||| ||eng d
020 $a 9798684634536
035 $a (MiAaPQ)AAI28236240
035 $a (MiAaPQ)vireoharvard3032Rischard
035 $a AAI28236240
040 $a MiAaPQ $c MiAaPQ
100 1 $a Rischard, Maxime. $3 3558950
245 1 0 $a Considering Uncertainty in Spatial Models: Causal Inference, Missing Data Imputations, and Model Comparison.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 182 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-06, Section: B.
500 $a Advisor: Miratrix, Luke.
502 $a Thesis (Ph.D.)--Harvard University, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a I present two applications of spatial and spatiotemporal models that stretch the traditional domain of these models, and a method for estimating the marginal likelihood in Bayesian models. Chapter 1 presents a spatial model for causal inference, in the setting of geographic regression discontinuity designs. The model is used to derive estimators of the treatment effect along a geographical border, to develop sensible estimands and estimators of the average treatment effect that account for the topology of the border, and to derive valid hypothesis tests. Chapter 2 addresses a bias in historical records of daily temperature minima and maxima due to the time of day at which the measurements are made, by deploying a spatiotemporal model in a missing data imputation framework. This is enabled by a novel and easy to implement Markov Chain Monte Carlo (MCMC) technique to sample the imputations conditionally on the observed daily extrema. Chapter 3 proposes a method for unbiasedly estimating the log ratio of normalizing constants of two unnormalized distributions, which can be used to estimate the marginal log likelihood of Bayesian models and for Bayesian cross-validation. These quantities are generally challenging to approximate from samples of the posterior, but are important for Bayesian model comparison and selection. I demonstrate the method on a spatial log Gaussian Cox process model and on a high-dimensional Bayesian variable selection model, and discuss how the lack of bias unlocks parallelization on multiple computing units.
590 $a School code: 0084.
650 4 $a Statistics. $3 517247
650 4 $a Atmospheric sciences. $3 3168354
653 $a MCMC
653 $a Climate
653 $a Causal inference
653 $a Regression discontinuity designs
653 $a Model comparison
653 $a Model selection
690 $a 0463
690 $a 0725
690 $a 0501
710 2 $a Harvard University. $b Statistics. $3 2093214
773 0 $t Dissertations Abstracts International $g 82-06B.
790 $a 0084
791 $a Ph.D.
792 $a 2019
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28236240