Boosting First-Order Clauses for Large, Skewed Data Sets

2010 ◽  
pp. 166-177
Author(s):  
Louis Oliphant ◽  
Elizabeth Burnside ◽  
Jude Shavlik
Keyword(s):  
Data Sets ◽  
Skewed Data ◽  
First Order

Parametric modelling of turbulence

1990 ◽  
Vol 332 (1627) ◽  
pp. 439-455 ◽  
Keyword(s):  
Long Range ◽  
Three Dimensional ◽  
Large Data ◽  
Theoretical Work ◽  
Long Range Dependence ◽  
Dependence Structure ◽  
Data Sets ◽  
Parametric Modelling ◽  
First Order ◽  
Log Linear

Some steps are taken towards a parametric statistical model for the velocity and velocity derivative fields in stationary turbulence, building on the background of existing theoretical and empirical knowledge of such fields. While the ultimate goal is a model for the three-dimensional velocity components, and hence for the corresponding velocity derivatives, we concentrate here on the stream wise velocity component. Discrete and continuous time stochastic processes of the first-order autoregressive type and with one-dimensional marginals having log-linear tails are constructed and compared with two large data-sets. It turns out that a first-order autoregression that fits the local correlation structure well is not capable of describing the correlations over longer ranges. A good fit locally as well as at longer ranges is achieved by using a process that is the sum of two independent autoregressions. We study this type of model in some detail. We also consider a model derived from the above-mentioned autoregressions and with dependence structure on the borderline to long-range dependence. This model is obtained by means of a general method for construction of processes with long-range dependence. Some suggestions for future empirical and theoretical work are given.

Potential fields and their partial derivatives produced by a 2D homogeneous polygonal source: A summary with some revisions

Geophysics ◽  
2011 ◽  
Vol 76 (4) ◽  
pp. L29-L34 ◽  
Author(s):  
Zhen Jia ◽  
Shiguo Wu
Keyword(s):  
Magnetic Field ◽  
Cross Section ◽  
Observation Point ◽  
Potential Fields ◽  
Data Sets ◽  
Partial Derivatives ◽  
First Order ◽  
Gravity And Magnetic ◽  
Derivatives Of ◽  
The Magnetic Field

We summarized and revised the present forward modeling methods for calculating the gravity- and magnetic-field components and their partial derivatives of a 2D homogeneous source with a polygonal cross section. The responses of interest include the gravity-field components and their first- and second-order partial derivatives and the magnetic-field components and their first-order partial derivatives. The revised formulas consist of several basic quantities that are common in all the formulations. A singularity appears when the observation point coincides with a polygon vertex. This singularity is removable for the gravity formulas but not for the others. The compact forms of the revised formulas make them easy to implement. We compare the gravity- and magnetic-field components and their partial derivatives produced by a 2D prism whose polygonal cross section approximates a cylinder with the corresponding analytical fields and partial derivatives of the cylinder. The perfect fittings presented by both data sets confirm the reliability of the updated formulas.

scholarly journals Estimating fast and slow reacting components in surface water and groundwater using a two-reactant model

2016 ◽  
Vol 9 (1) ◽  
pp. 19-25
Author(s):  
Priyanka Jamwal ◽  
M. N. Naveen ◽  
Yusuf Javeed
Keyword(s):  
Surface Water ◽  
Water Distribution ◽  
Distribution Networks ◽  
Bulk Water ◽  
Data Sets ◽  
Decay Data ◽  
First Order ◽  
Surface Water And Groundwater ◽  
Inorganic Matter ◽  
Chlorine Decay

Abstract. Maintaining residual chlorine levels in a water distribution network is a challenging task, especially in the context of developing countries where water is usually supplied intermittently. To model chlorine decay in water distribution networks, it is very important to understand chlorine kinetics in bulk water. Recent studies have suggested that chlorine decay rate depends on initial chlorine levels and the type of organic and inorganic matter present in water, indicating that a first-order decay model is unable to accurately predict chlorine decay in bulk water. In this study, we employed the two-reactant (2R) model to estimate the fast and slow reacting components in surface water and groundwater. We carried out a bench-scale test for surface water and groundwater at initial chlorine levels of 1, 2, and 5 mg L−1. We used decay data sets to estimate optimal parameter values for both surface water and groundwater. After calibration, the 2R model was validated with two decay data sets with varying initial chlorine concentrations (ICCs). This study arrived at three important findings. (a) We found that the ratio of slow to fast reacting components in groundwater was 30 times greater than that of the surface water. This observation supports the existing literature which indicates the presence of high levels of slow reacting fractions (manganese and aromatic hydrocarbons) in groundwater. (b) Both for surface water and groundwater, we obtained good model prediction, explaining 97 % of the variance in data for all cases. The mean square error obtained for the decay data sets was close to the instrument error, indicating the feasibility of the 2R model for chlorine prediction in both types of water. (c) In the case of deep groundwater, for high ICC levels (> 2 mg L−1), the first-order model can accurately predict chlorine decay in bulk water.

scholarly journals Climate seasonality limits carbon assimilation and storage in tropical forests

2016 ◽  
Author(s):  
Fabien H. Wagner ◽  
Bruno Hérault ◽  
Damien Bonal ◽  
Clément Stahl ◽  
Liana O. Anderson ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Tropical Forests ◽  
Vegetation Index ◽  
Photosynthetic Capacity ◽  
Carbon Assimilation ◽  
Terrestrial Ecosystem ◽  
Data Sets ◽  
First Order ◽  
And Storage ◽  
Productivity Measurements

Abstract. The seasonal climate drivers of the carbon cycle in tropical forests remain poorly known, although these forests account for more carbon assimilation and storage than any other terrestrial ecosystem. Based on a unique combination of seasonal pan-tropical data sets from 89 experimental sites (68 include aboveground wood productivity measurements and 35 litter productivity measurements), their associate canopy photosynthetic capacity (enhanced vegetation index, EVI) and climate, we ask how carbon assimilation and aboveground allocation are related to climate seasonality in tropical forests and how they interact in the seasonal carbon cycle. We found that canopy photosynthetic capacity seasonality responds positively to precipitation when rainfall is < 2000 mm.yr−1 (water-limited forests) and to radiation otherwise (light-limited forests); on the other hand, independent of climate limitations, wood productivity and litterfall are driven by seasonal variation in precipitation and evapotranspiration respectively. Consequently, light-limited forests present an asynchronism between canopy photosynthetic capacity and wood productivity. Precipitation first-order control indicates an overall decrease in tropical forest productivity in a drier climate.

scholarly journals Estimation of Change-point and Post-change Parameters after Adaptive Sequential CUSUM Test in an Exponential Family

2016 ◽  
Vol 5 (5) ◽  
pp. 43 ◽  
Author(s):  
Yanhong Wu
Keyword(s):  
Change Point ◽  
Exponential Family ◽  
River Flow ◽  
Temperature Data ◽  
Nile River ◽  
Data Sets ◽  
Cusum Test ◽  
First Order ◽  
Average Global Temperature ◽  
Cusum Procedure

In this paper, we consider an adaptive sequential CUSUM procedure in an exponential family where the change-point and post-change parameters are estimated adaptively. It is shown that the adaptive CUSUM procedure is efficient at the first order. The conditional biases of the estimation for the change-point and post-change parameter are studied. Comparison with the classical CUSUM procedure in the normal case is made. Nile river flow and average global temperature data sets are used for demonstration.

Enhancing the Performance of SVM on Skewed Data Sets by Exciting Support Vectors

2012 ◽  
pp. 101-110 ◽  
Author(s):  
José Hernández Santiago ◽  
Jair Cervantes ◽  
Asdrúbal López-Chau ◽  
Farid García Lamont
Keyword(s):  
Data Sets ◽  
Skewed Data ◽  
Support Vectors
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