Determining groundwater-surface water exchange from temperature-time series: Combining a local polynomial method with a maximum likelihood estimator

2015 ◽  
Vol 51 (2) ◽  
pp. 922-939 ◽  
Author(s):  
G. Vandersteen ◽  
U. Schneidewind ◽  
C. Anibas ◽  
C. Schmidt ◽  
P. Seuntjens ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Water Exchange ◽  
Temperature Time Series ◽  
Polynomial Method ◽  
Likelihood Estimator ◽  
Local Polynomial ◽  
Temperature Time

scholarly journals The effect of streambed heterogeneity on groundwater-surface water exchange fluxes inferred from temperature time series

2015 ◽  
Vol 51 (1) ◽  
pp. 198-212 ◽  
Author(s):  
Dylan J. Irvine ◽  
Roger H. Cranswick ◽  
Craig T. Simmons ◽  
Margaret A. Shanafield ◽  
Laura K. Lautz
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Water Exchange ◽  
Temperature Time Series ◽  
Temperature Time ◽  
Streambed Heterogeneity

scholarly journals Random Coefficient Models for Time-Series—Cross-Section Data: Monte Carlo Experiments

2007 ◽  
Vol 15 (2) ◽  
pp. 182-195 ◽  
Author(s):  
Nathaniel Beck ◽  
Jonathan N. Katz
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Cross Section ◽  
Random Coefficient ◽  
Model Parameters ◽  
Cross Section Data ◽  
Likelihood Estimator ◽  
Random Coefficient Models ◽  
Section Data

This article considers random coefficient models (RCMs) for time-series—cross-section data. These models allow for unit to unit variation in the model parameters. The heart of the article compares the finite sample properties of the fully pooled estimator, the unit by unit (unpooled) estimator, and the (maximum likelihood) RCM estimator. The maximum likelihood estimator RCM performs well, even where the data were generated so that the RCM would be problematic. In an appendix, we show that the most common feasible generalized least squares estimator of the RCM models is always inferior to the maximum likelihood estimator, and in smaller samples dramatically so.

scholarly journals Maximum likelihood estimation for cooperative sequential adsorption

2009 ◽  
Vol 41 (04) ◽  
pp. 978-1001 ◽  
Author(s):  
Mathew D. Penrose ◽  
Vadim Shcherbakov
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Maximum Likelihood Estimator ◽  
Thermodynamic Limit ◽  
Likelihood Estimation ◽  
Likelihood Estimator ◽  
Current Configuration ◽  
Sequential Adsorption ◽  
Spatial Locations

We consider a model for a time series of spatial locations, in which points are placed sequentially at random into an initially empty region of ℝ d , and given the current configuration of points, the likelihood at location x for the next particle is proportional to a specified function β k of the current number (k) of points within a specified distance of x. We show that the maximum likelihood estimator of the parameters β k (assumed to be zero for k exceeding some fixed threshold) is consistent in the thermodynamic limit where the number of points grows in proportion to the size of the region.

scholarly journals Maximum likelihood estimation for cooperative sequential adsorption

2009 ◽  
Vol 41 (4) ◽  
pp. 978-1001 ◽  
Author(s):  
Mathew D. Penrose ◽  
Vadim Shcherbakov
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Maximum Likelihood Estimator ◽  
Thermodynamic Limit ◽  
Likelihood Estimation ◽  
Likelihood Estimator ◽  
Current Configuration ◽  
Sequential Adsorption ◽  
Spatial Locations

We consider a model for a time series of spatial locations, in which points are placed sequentially at random into an initially empty region of ℝd, and given the current configuration of points, the likelihood at location x for the next particle is proportional to a specified function βk of the current number (k) of points within a specified distance of x. We show that the maximum likelihood estimator of the parameters βk (assumed to be zero for k exceeding some fixed threshold) is consistent in the thermodynamic limit where the number of points grows in proportion to the size of the region.

scholarly journals Asymptotic normality of the maximum likelihood estimator for cooperative sequential adsorption

2011 ◽  
Vol 43 (3) ◽  
pp. 636-648 ◽  
Author(s):  
Mathew D. Penrose ◽  
Vadim Shcherbakov
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Asymptotic Normality ◽  
Maximum Likelihood Estimator ◽  
Time Series Data ◽  
Series Data ◽  
Adsorption Model ◽  
Likelihood Estimator ◽  
Large Samples ◽  
Sequential Adsorption

We consider statistical inference for a parametric cooperative sequential adsorption model for spatial time series data, based on maximum likelihood. We establish asymptotic normality of the maximum likelihood estimator in the thermodynamic limit. We also perform and discuss some numerical simulations of the model, which illustrate the procedure for creating confidence intervals for large samples.

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