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

Author(s):  
Xiaomeng Zhang ◽  
Xinyu Zhang ◽  
Yanyuan Ma
Blood ◽  
2010 ◽  
Vol 116 (14) ◽  
pp. 2472-2475 ◽  
Author(s):  
Thierry Bonnefoix ◽  
Mary Callanan

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.


2009 ◽  
Vol 39 (1) ◽  
pp. 1-33 ◽  
Author(s):  
Gareth W. Peters ◽  
Pavel V. Shevchenko ◽  
Mario V. Wüthrich

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.


Author(s):  
Lorenzo Bencivelli ◽  
Massimiliano Giuseppe Marcellino ◽  
Gianluca Moretti

2020 ◽  
Vol 42 (1) ◽  
pp. 37-103
Author(s):  
Hardik A. Marfatia

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.


Sign in / Sign up

Export Citation Format

Share Document