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.

scholarly journals Maximum Likelihood Estimation of the VAR(1) Model Parameters with Missing Observations

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Helena Mouriño ◽  
Maria Isabel Barão
Keyword(s):  
Stochastic Process ◽  
Missing Data ◽  
Maximum Likelihood ◽  
Moving Average ◽  
Practical Importance ◽  
Likelihood Estimation ◽  
Model Parameters ◽  
Missing Observations ◽  
Data Set ◽  
The Impact

Missing-data problems are extremely common in practice. To achieve reliable inferential results, we need to take into account this feature of the data. Suppose that the univariate data set under analysis has missing observations. This paper examines the impact of selecting an auxiliary complete data set—whose underlying stochastic process is to some extent interdependent with the former—to improve the efficiency of the estimators for the relevant parameters of the model. The Vector AutoRegressive (VAR) Model has revealed to be an extremely useful tool in capturing the dynamics of bivariate time series. We propose maximum likelihood estimators for the parameters of the VAR(1) Model based on monotone missing data pattern. Estimators’ precision is also derived. Afterwards, we compare the bivariate modelling scheme with its univariate counterpart. More precisely, the univariate data set with missing observations will be modelled by an AutoRegressive Moving Average (ARMA(2,1)) Model. We will also analyse the behaviour of the AutoRegressive Model of order one, AR(1), due to its practical importance. We focus on the mean value of the main stochastic process. By simulation studies, we conclude that the estimator based on the VAR(1) Model is preferable to those derived from the univariate context.

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

scholarly journals Climate, snow, and soil moisture data set for the Tuolumne and Merced river watersheds, California, USA

2019 ◽  
Vol 11 (1) ◽  
pp. 101-110 ◽  
Author(s):  
James W. Roche ◽  
Robert Rice ◽  
Xiande Meng ◽  
Daniel R. Cayan ◽  
Michael D. Dettinger ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Sierra Nevada ◽  
Land Surface ◽  
Forest Canopy ◽  
Snow Water Equivalent ◽  
Process Models ◽  
Series Data ◽  
Climate Data ◽  
Data Set ◽  
Data Repositories

Abstract. We present hourly climate data to force land surface process models and assessments over the Merced and Tuolumne watersheds in the Sierra Nevada, California, for the water year 2010–2014 period. Climate data (38 stations) include temperature and humidity (23), precipitation (13), solar radiation (8), and wind speed and direction (8), spanning an elevation range of 333 to 2987 m. Each data set contains raw data as obtained from the source (Level 0), data that are serially continuous with noise and nonphysical points removed (Level 1), and, where possible, data that are gap filled using linear interpolation or regression with a nearby station record (Level 2). All stations chosen for this data set were known or documented to be regularly maintained and components checked and calibrated during the period. Additional time-series data included are available snow water equivalent records from automated stations (8) and manual snow courses (22), as well as distributed snow depth and co-located soil moisture measurements (2–6) from four locations spanning the rain–snow transition zone in the center of the domain. Spatial data layers pertinent to snowpack modeling in this data set are basin polygons and 100 m resolution rasters of elevation, vegetation type, forest canopy cover, tree height, transmissivity, and extinction coefficient. All data are available from online data repositories (https://doi.org/10.6071/M3FH3D).

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.

Point-process models with linearly parametrized intensity for application to earthquake data

1986 ◽  
Vol 23 (A) ◽  
pp. 291-310 ◽  
Author(s):  
Yosihiko Ogata ◽  
Koichi Katsura
Keyword(s):  
Seismic Activity ◽  
Process Models ◽  
Evolutionary Trend ◽  
Conditional Intensity ◽  
Data Set ◽  
Technical Aspects ◽  
Point Process Models ◽  
Earthquake Sequences ◽  
Flexible Models ◽  
Earthquake Data

It is demonstrated that linear parametrization of the conditional intensity provides systematic classes of flexible models which are reasonably useful for calculating maximum likelihoods. To exemplify the modelling, seismic activity around Canberra is decomposed into components of evolutionary trend, clustering and periodicity. The causal relationship between earthquake sequences from two seismic regions is also analysed for a certain Japanese earthquake data set.Some technical aspects of the modelling and calculations are described.

scholarly journals Cure fraction models using mixture and non-mixture models

2012 ◽  
Vol 51 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Jorge A. Achcar ◽  
Emílio A. Coelho-Barros ◽  
Josmar Mazucheli
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Mixture Models ◽  
Censored Data ◽  
Life Time ◽  
Time Data ◽  
Data Set ◽  
Cure Fraction ◽  
The Bayesian Approach

ABSTRACT We introduce the Weibull distributions in presence of cure fraction, censored data and covariates. Two models are explored in this paper: mixture and non-mixture models. Inferences for the proposed models are obtained under the Bayesian approach, using standard MCMC (Markov Chain Monte Carlo) methods. An illustration of the proposed methodology is given considering a life- time data set.

Evaluating the accuracy of biodiversity changes through geologic times: from simulation to solution

Paleobiology ◽  
2017 ◽  
Vol 43 (4) ◽  
pp. 667-692 ◽  
Author(s):  
Corentin Gibert ◽  
Gilles Escarguel
Keyword(s):  
Discrete Time ◽  
Continuous Time ◽  
Simple Algorithm ◽  
Time Discretization ◽  
Time Interval ◽  
Taxonomic Richness ◽  
Time Data ◽  
Worst Case ◽  
Data Set ◽  
Reconstructed Signal

AbstractEstimating biodiversity and its variations through geologic time is a notoriously difficult task, due to several taphonomic and methodological effects that make the reconstructed signal potentially distinct from the unknown, original one. Through a simulation approach, we examine the effect of a major, surprisingly still understudied, source of potential disturbance: the effect of time discretization through biochronological construction, which generates spurious coexistences of taxa within discrete time intervals (i.e., biozones), and thus potentially makes continuous- and discrete-time biodiversity curves very different. Focusing on the taxonomic-richness dimension of biodiversity (including estimates of origination and extinction rates), our approach relies on generation of random continuous-time richness curves, which are then time-discretized to estimate the noise generated by this manipulation. A broad spectrum of data-set parameters (including average taxon longevity and biozone duration, total number of taxa, and simulated time interval) is evaluated through sensitivity analysis. We show that the deteriorating effect of time discretization on the richness signal depends highly on such parameters, most particularly on average biozone duration and taxonomic longevity because of their direct relationship with the number of false coexistences generated by time discretization. With several worst-case but realistic parameter combinations (e.g., when relatively short-lived taxa are analyzed in a long-ranging biozone framework), the original and time-discretized richness curves can ultimately show a very weak to zero correlation, making these two time series independent. Based on these simulation results, we propose a simple algorithm allowing the back-transformation of a discrete-time taxonomic-richness data set, as customarily constructed by paleontologists, into a continuous-time data set. We show that the reconstructed richness curve obtained this way fits the original signal much more closely, even when the parameter combination of the original data set is particularly adverse to an effective time-discretized reconstruction.

Primary Production in a Tidally Energetic Fjord: Evidence of Seasonal and Inter-annual Tidal Forcing

1989 ◽  
Vol 46 (12) ◽  
pp. 2166-2172 ◽  
Author(s):  
T. A. Kessler ◽  
T. R. Parsons
Keyword(s):  
Primary Production ◽  
Standing Stock ◽  
Covariance Structure ◽  
Static Stability ◽  
Data Set ◽  
Annual Variations ◽  
Basin Surface ◽  
Statistical Relationships ◽  
Inflow Jet

A long term data set collected from a tidally energetic sill fjord was analyzed for its statistical relationships between primary production indices and several represented environmental variables. The analysis identified variance and covariance structure in these variables implicating changes in water column clarity, in inter-annual variability of phytoplankton carbon uptake rate, and the static stability of basin surface water in phytoplankton standing stock. The biomass–stability relationship was found to be seasonally dependent, with biomass positively correlated with stability in the summer and negatively correlated in the spring/fall, and restricted to waters under the direct mixing influence of the tidal inflow jet. These statistical patterns are discussed in terms of a possible control of primary production by seasonal and inter-annual variations in tidal inflow buoyancy.

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