Evaluation of Hantush’s S Function Estimation Methods for Predicting Rise in Water Table

2019 ◽  
Vol 33 (7) ◽  
pp. 2239-2260
Author(s):  
Shakir Ali ◽  
Adlul Islam
Keyword(s):  
Water Table ◽  
Estimation Methods ◽  
Function Estimation

Long‐Term Earthquake Hazard in North China Estimated from a Modern Catalog

2019 ◽  
Vol 109 (6) ◽  
pp. 2340-2355 ◽  
Author(s):  
Ziyao Xiong ◽  
Jiancang Zhuang ◽  
Shiyong Zhou
Keyword(s):  
Bayesian Method ◽  
North China ◽  
Voronoi Tessellation ◽  
Earthquake Hazard ◽  
B Value ◽  
Estimation Methods ◽  
Earthquake Catalog ◽  
Function Estimation ◽  
Simulation Experiments

Abstract In this study, to obtain optimal estimates of the earthquake hazard in North China based on the modern earthquake catalog, we used two variable kernel function estimation methods, proposed by Stock and Smith, and Zhuang, the Bayesian Delaunay tessellation smoothing method by Ogata (ODTB), and a newly proposed incomplete centroidal Voronoi tessellation (ICVT) method, to calculate the total and background seismic spatial occurrence rates for the study area. The sophisticated ODTB method is more stable than the others, but is relatively expensive, in terms of computation demands, whereas Zhuang et al.’s kernel estimate and the new ICVT method are able to provide reasonable estimates and easier to implement. We also calculated the spatial variations of the b‐value, using the Bayesian method with smoothness prior proposed by Ogata. Using comparative analyses and simulation experiments, we show that all of the methods give similar spatial patterns of seismic occurrences.

scholarly journals Estimating geostatistical parameters and spatially-variable hydraulic conductivity within a catchment system using an ensemble smoother

2011 ◽  
Vol 8 (5) ◽  
pp. 9587-9635
Author(s):  
R. T. Bailey ◽  
D. Baù
Keyword(s):  
Parameter Estimation ◽  
Spatial Distribution ◽  
Hydraulic Conductivity ◽  
Water Table ◽  
Subsurface Flow ◽  
Estimation Methods ◽  
Future Research ◽  
Ensemble Smoother ◽  
Streamflow Data ◽  
Parameter Values

Abstract. Groundwater flow models are important tools in assessing baseline conditions and investigating management alternatives in groundwater systems. The usefulness of these models, however, is often hindered by insufficient knowledge regarding the magnitude and spatial distribution of the spatially-distributed parameters, such as hydraulic conductivity (K), that govern the response of these models. Proposed parameter estimation methods frequently are demonstrated using simplified aquifer representations, when in reality the groundwater regime in a given watershed is influenced by strongly-coupled surface-subsurface processes. Furthermore, parameter estimation methodologies that rely on a geostatistical structure of K often assume the parameter values of the geostatistical model as known or estimate these values from limited data. In this study, we investigate the use of a data assimilation algorithm, the Ensemble Smoother, to provide enhanced estimates of K within a catchment system using the fully-coupled, surface-subsurface flow model CATHY. Both water table elevation and streamflow data are assimilated to condition the spatial distribution of K. An iterative procedure using the ES update routine, in which geostatistical parameter values defining the true spatial structure of K are identified, is also presented. In this procedure, parameter values are inferred from the updated ensemble of K fields and used in the subsequent iteration to generate the K ensemble, with the process proceeding until parameter values are converged upon. The parameter estimation scheme is demonstrated via a synthetic three-dimensional tilted v-shaped catchment system incorporating stream flow and variably-saturated subsurface flow, with spatio-temporal variability in forcing terms. Results indicate that the method is successful in providing improved estimates of the K field, and that the iterative scheme can be used to identify the geostatistical parameter values of the aquifer system. In general, water table data have a much greater ability than streamflow data to condition K. Future research includes applying the methodology to an actual regional study site.

scholarly journals Independence-aware Advantage Estimation

2021 ◽  
Author(s):  
Pushi Zhang ◽  
Li Zhao ◽  
Guoqing Liu ◽  
Jiang Bian ◽  
Minlie Huang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Reinforcement Learning ◽  
Importance Sampling ◽  
Time Horizon ◽  
Estimation Methods ◽  
Function Estimation ◽  
Complex Environments ◽  
Estimation Variance ◽  
Independence Property ◽  
Current Action

Most of existing advantage function estimation methods in reinforcement learning suffer from the problem of high variance, which scales unfavorably with the time horizon. To address this challenge, we propose to identify the independence property between current action and future states in environments, which can be further leveraged to effectively reduce the variance of the advantage estimation. In particular, the recognized independence property can be naturally utilized to construct a novel importance sampling advantage estimator with close-to-zero variance even when the Monte-Carlo return signal yields a large variance. To further remove the risk of the high variance introduced by the new estimator, we combine it with existing Monte-Carlo estimator via a reward decomposition model learned by minimizing the estimation variance. Experiments demonstrate that our method achieves higher sample efficiency compared with existing advantage estimation methods in complex environments.

A Structural Model of a Multitasking Salesforce: Incentives, Private Information, and Job Design

2021 ◽  
Author(s):  
Minkyung Kim ◽  
K. Sudhir ◽  
Kosuke Uetake
Keyword(s):  
Adverse Selection ◽  
Private Information ◽  
Structural Model ◽  
Job Design ◽  
Estimation Methods ◽  
Efficiency Gain ◽  
Function Estimation ◽  
Task Specialization ◽  
Heterogeneous Effects ◽  
Two Step Estimation

This paper broadens the focus of empirical research on salesforce management to include multitasking settings with multidimensional incentives, where salespeople have private information about customers. This allows us to ask novel substantive questions around multidimensional incentive design and job design while managing the costs and benefits of private information. To this end, the paper introduces the first structural model of a multitasking salesforce in response to multidimensional incentives. The model also accommodates (i) dynamic intertemporal tradeoffs in effort choice across the tasks and (ii) salesperson’s private information about customers. We apply our model in a rich empirical setting in microfinance and illustrate how to address various identification and estimation challenges. We extend two-step estimation methods used for unidimensional compensation plans by embedding a flexible machine learning (random forest) model in the first-stage multitasking policy function estimation within an iterative procedure that accounts for salesperson heterogeneity and private information. Estimates reveal two latent segments of salespeople—a hunter segment that is more efficient in loan acquisition and a farmer segment that is more efficient in loan collection. Counterfactuals reveal heterogeneous effects: hunters’ private information hurts the firm as they engage in adverse selection; farmers’ private information helps the firm as they use it to better collect loans. The payoff complementarity induced by multiplicative incentive aggregation softens adverse specialization by hunters relative to additive aggregation but hurts performance among farmers. Overall, task specialization in job design for hunters (acquisition) and farmers (collection) hurts the firm as adverse selection harm overwhelms efficiency gain. This paper was accepted by Duncan Simester, marketing.

scholarly journals The Substitution Elasticity, Factor Shares, and the Low-Frequency Panel Model

2017 ◽  
Vol 9 (4) ◽  
pp. 225-253 ◽  
Author(s):  
Robert S. Chirinko ◽  
Debdulal Mallick
Keyword(s):  
Low Frequency ◽  
Estimation Methods ◽  
Function Estimation ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Long Run ◽  
New Strategy ◽  
Low Pass ◽  
Secular Decline ◽  
Production Function Estimation

The value of the elasticity of substitution between labor and capital (σ) is a crucial assumption in understanding the secular decline in the labor share of income. This paper develops and implements a new strategy for estimating this crucial parameter by combining a low-pass filter with panel data to identify the low-frequency/long-run relations appropriate to production function estimation. Standard estimation methods, which do not filter out transitory variation, generate downwardly biased estimates of 40 percent to 70 percent relative to the benchmark value. Despite correcting for this bias, our preferred estimate of 0.40 is substantially below the Cobb-Douglas assumption of σ = 1. (JEL C51, E22, E24, E25, O41)

Predicting mortality using two renal function estimation methods in hospitalised stroke patients

2010 ◽  
Vol 139 (3) ◽  
pp. 307-309 ◽  
Author(s):  
Louise F. Porter ◽  
Miles D. Witham ◽  
Callum G. Fraser ◽  
Ronald S. MacWalter
Keyword(s):  
Renal Function ◽  
Estimation Methods ◽  
Function Estimation ◽  
Stroke Patients

scholarly journals Influence of Kidney Function Estimation Methods on Eligibility for Edoxaban Population Impact of the US Food and Drug Administration’s Approach for Its Product Labeling

Circulation ◽  
2016 ◽  
Vol 134 (15) ◽  
pp. 1122-1124 ◽  
Author(s):  
Sean D. Pokorney ◽  
Peter Shrader ◽  
Laine Thomas ◽  
Gregg C. Fonarow ◽  
Peter R. Kowey ◽  
...  
Keyword(s):  
Kidney Function ◽  
Estimation Methods ◽  
Function Estimation ◽  
Product Labeling ◽  
Population Impact ◽  
The Us

scholarly journals Estimating geostatistical parameters and spatially-variable hydraulic conductivity within a catchment system using an ensemble smoother

2012 ◽  
Vol 16 (2) ◽  
pp. 287-304 ◽  
Author(s):  
R. T. Bailey ◽  
D. Baù
Keyword(s):  
Parameter Estimation ◽  
Spatial Distribution ◽  
Hydraulic Conductivity ◽  
Water Table ◽  
Subsurface Flow ◽  
Estimation Methods ◽  
Future Research ◽  
Ensemble Smoother ◽  
Streamflow Data ◽  
Parameter Values

Abstract. Groundwater flow models are important tools in assessing baseline conditions and investigating management alternatives in groundwater systems. The usefulness of these models, however, is often hindered by insufficient knowledge regarding the magnitude and spatial distribution of the spatially-distributed parameters, such as hydraulic conductivity (K), that govern the response of these models. Proposed parameter estimation methods frequently are demonstrated using simplified aquifer representations, when in reality the groundwater regime in a given watershed is influenced by strongly-coupled surface-subsurface processes. Furthermore, parameter estimation methodologies that rely on a geostatistical structure of K often assume the parameter values of the geostatistical model as known or estimate these values from limited data. In this study, we investigate the use of a data assimilation algorithm, the Ensemble Smoother, to provide enhanced estimates of K within a catchment system using the fully-coupled, surface-subsurface flow model CATHY. Both water table elevation and streamflow data are assimilated to condition the spatial distribution of K. An iterative procedure using the ES update routine, in which geostatistical parameter values defining the true spatial structure of K are identified, is also presented. In this procedure, parameter values are inferred from the updated ensemble of K fields and used in the subsequent iteration to generate the K ensemble, with the process proceeding until parameter values are converged upon. The parameter estimation scheme is demonstrated via a synthetic three-dimensional tilted v-shaped catchment system incorporating stream flow and variably-saturated subsurface flow, with spatio-temporal variability in forcing terms. Results indicate that the method is successful in providing improved estimates of the K field, and that the iterative scheme can be used to identify the geostatistical parameter values of the aquifer system. In general, water table data have a much greater ability than streamflow data to condition K. Future research includes applying the methodology to an actual regional study site.

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