scholarly journals Multivariate Stochastic Process Models for Correlated Responses of Mixed Type

2016 ◽  
Vol 11 (3) ◽  
pp. 797-820 ◽  
Author(s):  
Tony Pourmohamad ◽  
Herbert K. H. Lee
Keyword(s):  
Stochastic Process ◽  
Mixed Type ◽  
Process Models ◽  
Correlated Responses ◽  
Multivariate Stochastic Process

The effects of health histories on stochastic process models of aging and mortality

1995 ◽  
Vol 34 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Anatoli I. Yashin ◽  
Kenneth G. Manton ◽  
Max A. Woodbury ◽  
Eric Stallard
Keyword(s):  
Stochastic Process ◽  
Process Models

Replicating the Recovery following the 2014 South Napa Earthquake using Stochastic Process Models

2018 ◽  
Vol 34 (3) ◽  
pp. 1247-1266 ◽  
Author(s):  
Hua Kang ◽  
Henry V. Burton ◽  
Haoxiang Miao
Keyword(s):  
Stochastic Process ◽  
Repair Time ◽  
Process Models ◽  
Time Data ◽  
Data Set ◽  
Damage Data ◽  
Exogenous Factors ◽  
Recovery Models ◽  
Statistical Relationships ◽  
Recovery Trajectory

Post-earthquake recovery models can be used as decision support tools for pre-event planning. However, due to a lack of available data, there have been very few opportunities to validate and/or calibrate these models. This paper describes the use of building damage, permitting, and repair data from the 2014 South Napa Earthquake to evaluate a stochastic process post-earthquake recovery model. Damage data were obtained for 1,470 buildings, and permitting and repair time data were obtained for a subset (456) of those buildings. A “blind” prediction is shown to adequately capture the shape of the recovery trajectory despite overpredicting the overall pace of the recovery. Using the mean time to permit and repair time from the acquired data set significantly improves the accuracy of the recovery prediction. A generalized model is formulated by establishing statistical relationships between key time parameters and endogenous and exogenous factors that have been shown to influence the pace of recovery.

Models of Movement: A Review of Alternative Methods

1980 ◽  
Vol 12 (12) ◽  
pp. 1383-1404 ◽  
Author(s):  
A Pickles
Keyword(s):  
Stochastic Process ◽  
Transition Rate ◽  
Process Models ◽  
Alternative Methods ◽  
Occupancy Models ◽  
Alternative Approaches ◽  
Fixed State

This paper reviews methods available to analyse movement and in particular migration. Stochastic process models seem able to provide a framework for microanalysis which can incorporate much of the complexity of such processes. However, a consideration of the effect of macro-constraints, in the form of limited opportunities for movement and of interhousehold competition, leads to a distinction between fixed transition rate and fixed state occupancy models. Alternative approaches to fixed state occupancy models are considered, and some of their potential strengths and weaknesses are discussed.

On the limiting behaviour of a basic stochastic process

1972 ◽  
Vol 9 (1) ◽  
pp. 202-207
Author(s):  
İzzet Şahin ◽  
Oussama Achou
Keyword(s):  
Stochastic Process ◽  
Integral Equation ◽  
Stochastic Processes ◽  
Integral Equations ◽  
Mixed Type ◽  
Limiting Distributions ◽  
State Dependent ◽  
Limiting Behaviour ◽  
Rate Of Decline

Determination of the limiting distributions for a class of mixed-type stochastic processes with state-dependent rates of decline is reduced to the solution of a class of integral equations. For the case where the rate of decline is proportional to the state, some results are obtained by solving the integral equation of the process through Fuchs' method.

ANALYSIS OF PROTEIN ACTIVITY DATA BY GAUSSIAN STOCHASTIC PROCESS MODELS

1999 ◽  
Vol 9 (1) ◽  
pp. 145-160 ◽  
Author(s):  
Nancy J. McMillan ◽  
Jerome Sacks ◽  
William J. Welch ◽  
Feng Gao
Keyword(s):  
Stochastic Process ◽  
Process Models ◽  
Protein Activity ◽  
Gaussian Stochastic Process ◽  
Activity Data
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