A model‐averaging treatment of multiple instruments in Poisson models with errors

2021 ◽  
Author(s):  
Xiaomeng Zhang ◽  
Xinyu Zhang ◽  
Yanyuan Ma
Keyword(s):  
Model Averaging ◽  
Poisson Models

scholarly journals Accurate hematopoietic stem cell frequency estimates by fitting multicell Poisson models substituting to the single-hit Poisson model in limiting dilution transplantation assays

Blood ◽  
2010 ◽  
Vol 116 (14) ◽  
pp. 2472-2475 ◽  
Author(s):  
Thierry Bonnefoix ◽  
Mary Callanan
Keyword(s):  
Stem Cell ◽  
Hematopoietic Stem Cell ◽  
Model Averaging ◽  
Poisson Model ◽  
Threshold Value ◽  
Threshold Level ◽  
Hematopoietic Stem ◽  
Gold Standard Method ◽  
Poisson Models ◽  
Limiting Dilution

Abstract Limiting dilution transplantation assay (LDTA) is considered as the gold standard method to assess hematopoietic stem cell (HSC) content. Traditionally, HSC frequency estimates are based on the single-hit Poisson model (SHPM), which posits that one donor HSC is sufficient to generate a progeny of detectable differentiated cells above a threshold value in hosts. However, there is no clear support for this statement, and it is receivable that more than one donor HSC may be necessary to provide detectable reconstitution in hosts above the threshold level for detection, usually 0.5% to 1% of donor-derived cells. To address this hypothesis, we evaluated the ability of a class of multiCell Poisson models (C≥1PMs) to fit to LDTAs. In 7 of the 8 reanalyzed LDTAs, C≥1PMs plausibly compete with the traditional SHPM. Model averaging across the set of plausible models gives 1.32- to 5.88-fold increases in HSC frequencies compared with the SHPM.

scholarly journals Model Uncertainty in Claims Reserving within Tweedie's Compound Poisson Models

2009 ◽  
Vol 39 (1) ◽  
pp. 1-33 ◽  
Author(s):  
Gareth W. Peters ◽  
Pavel V. Shevchenko ◽  
Mario V. Wüthrich
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Model Uncertainty ◽  
Model Averaging ◽  
Poisson Model ◽  
Compound Poisson ◽  
Poisson Models ◽  
Model Selection Problem

AbstractIn this paper we examine the claims reserving problem using Tweedie's compound Poisson model. We develop the maximum likelihood and Bayesian Markov chain Monte Carlo simulation approaches to fit the model and then compare the estimated models under different scenarios. The key point we demonstrate relates to the comparison of reserving quantities with and without model uncertainty incorporated into the prediction. We consider both the model selection problem and the model averaging solutions for the predicted reserves. As a part of this process we also consider the sub problem of variable selection to obtain a parsimonious representation of the model being fitted.

Selecting Predictors by Using Bayesian Model Averaging in Bridge Models

2012 ◽  
Author(s):  
Lorenzo Bencivelli ◽  
Massimiliano Giuseppe Marcellino ◽  
Gianluca Moretti
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Bridge Models

Forecasting Interconnections in International Housing Markets: Evidence from the Dynamic Model Averaging Approach

2020 ◽  
Vol 42 (1) ◽  
pp. 37-103
Author(s):  
Hardik A. Marfatia
Keyword(s):  
Dynamic Model ◽  
Model Averaging ◽  
Housing Markets ◽  
The United States ◽  
Forecasting Model ◽  
Change Over Time ◽  
The United Kingdom ◽  
Novel Approach ◽  
Dynamic Model Averaging ◽  
Over Time

In this paper, I undertake a novel approach to uncover the forecasting interconnections in the international housing markets. Using a dynamic model averaging framework that allows both the coefficients and the entire forecasting model to dynamically change over time, I uncover the intertwined forecasting relationships in 23 leading international housing markets. The evidence suggests significant forecasting interconnections in these markets. However, no country holds a constant forecasting advantage, including the United States and the United Kingdom, although the U.S. housing market's predictive power has increased over time. Evidence also suggests that allowing the forecasting model to change is more important than allowing the coefficients to change over time.

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