The Experts below are selected from a list of 135 Experts worldwide ranked by ideXlab platform
Nicholas J Cox - One of the best experts on this subject based on the ideXlab platform.
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speaking stata the protean Quantile Plot
Stata Journal, 2005Co-Authors: Nicholas J CoxAbstract:Quantile Plots showing by default ordered values versus cumulative probabilities are both well known and also often neglected, considering their major advantages. Their flexibility and power is emphasized by using the qPlot program to show several variants on the standard form, making full use of options for reverse, ranked, and transformed scales and for superimposing and juxtaposing Quantile traces. Examples are drawn from the analysis of species abundance data in ecology. A revised version of qPlot is formally released with this column. Distribution Plots in which the axes are interchanged are also discussed briefly, in conjunction with a revised version of distPlot, also released now.
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qgamma stata module to generate Quantile Quantile Plot for data vs fitted gamma distribution
Research Papers in Economics, 2003Co-Authors: Nicholas J CoxAbstract:qgamma Plots the Quantiles of a variable against the Quantiles of a two-parameter gamma distribution with a shape parameter and a scale parameter. Fitting is done quietly by gammafit, which should be installed separately. These programs all require Stata 8. Also included is the previous version, now renamed as qgamma4, which may be used in Stata 4 through 7. Estimation may be performed using gamma4, available as part of the gammafit package. Note: there is no connection between this package and official Stata command gamma.
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QQPlot2: Stata module to produce Quantile-Quantile Plot
Statistical Software Components, 1998Co-Authors: Nicholas J CoxAbstract:qqPlot2 Plots the Quantiles of varname1 against the Quantiles of varname2. qqPlot2 minutely generalises qqPlot in the sense that the user can control the main title through the title() option.
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quantil2 stata module to generate multivariate Quantile Plot
Statistical Software Components, 1998Co-Authors: Nicholas J CoxAbstract:-quantil2- produces a Plot of the ordered values of varlist against the so-called Plotting positions, which are essentially Quantiles of a uniform distribution on [0,1] for the same number of values. -quantil2- generalises -Quantile- to allow multiple variables, or a single variable classified by another variable; Plotting positions other than (i - 0.5) / n; and a wider variety of graphical choices. Installation of -_gpp- is needed. This is version 1.3.1 of the software, revised for Stata version 7 compatibility.
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qexp stata module to produce Quantile Quantile Plot for data vs fitted exponential distribution
Statistical Software Components, 1998Co-Authors: Nicholas J CoxAbstract:qexp Plots the Quantiles of varname against the Quantiles of a one-parameter exponential distribution, with distribution function 1 - exp(-varname / mean). The values of varname should be zero or positive.
Philippe Poggi - One of the best experts on this subject based on the ideXlab platform.
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an m rice wind speed frequency distribution
Wind Energy, 2011Co-Authors: Rachel Baile, Jeanfrancois Muzy, Philippe PoggiAbstract:Several known statistical distributions can describe wind speed data, the most commonly used being the Weibull family. In this paper, a new law, called "M-Rice", is proposed for modeling wind speed frequency distributions. Inspired by recent empirical findings that suggest the existence of some cascading process in the mesoscale range, we consider that wind speed can be described by a seasonal ARMA model where the noise term is "multifractal" i.e., associated with a random cascade. This leads to wind speeds distributed according to M-Rice pdf, i.e. a Rice distribution multiplicatively convolved with a normal law. Comparison based on the estimation of the mean wind speed and power density values as well as different goodness-of-fit tests (Kolmogorov-Smirnov, Kuiper test and the Quantile-Quantile Plot) is made between this new distribution and the Weibull one, for 35 data sets of wind speed from Netherlands and Corsica (France) sites. Accordingly, the M-Rice and Weibull distributions provide comparable performances, while q-q Plots suggest that M-rice distribution provides a better fit of extreme wind speed data. Beyond these good results, our approach allows one to interpret the observed values of Weibull parameters.
Kefa Zhou - One of the best experts on this subject based on the ideXlab platform.
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assessment of geochemical anomaly uncertainty through geostatistical simulation and singularity analysis
Natural resources research, 2019Co-Authors: Yue Liu, Qiuming Cheng, Emmanuel John M Carranza, Kefa ZhouAbstract:Geochemical anomalies are commonly separated into different geochemical anomaly levels based on one or more thresholds. However, this practice may cause some important geochemical anomaly information to be lost and subsequently draw wrong decisions for mineral exploration. In addition, previous studies indicate that sparse geochemical sampling always entails uncertainty resulting from conventional geochemical interpolation methods because of smoothing effect. Uncertainty can propagate through the various steps of geochemical data analysis that may lead to significant impact on the final results (e.g., anomaly interpretation and mineral exploration). For geochemical anomaly identification, quantifying the probability of unsampled locations and characterizing the spatial uncertainty of geochemical anomaly based on (not) exceeding a key threshold is very important for practical demands such as exploration risk assessment. Considering the limitations of deterministic modeling method and geochemical anomaly assessment, this study proposes a new method of geochemical anomaly uncertainty assessment by combining geostatistical simulation and singularity analysis. A case study for Au anomaly uncertainty assessment is presented in the west Tianshan region (China) so as to verify the feasibility and effectiveness of the proposed method. The sequential Gaussian simulation was adopted to generate a set of equiprobable realizations that were subsequently employed to produce a series of corresponding singularity index realizations by means of singularity analysis. Critical thresholds of E-type singularity index (α) were determined by the method of singularity-Quantile Plot analysis, which were used to simulate the spatial uncertainty of Au anomaly in the study area. The results show that the risk probability of Au anomaly characterized by (not) exceedance of a critical threshold can be considered as an important reference for exploration decision-making and risk management.
Emmanuel John M Carranza - One of the best experts on this subject based on the ideXlab platform.
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assessment of geochemical anomaly uncertainty through geostatistical simulation and singularity analysis
Natural resources research, 2019Co-Authors: Yue Liu, Qiuming Cheng, Emmanuel John M Carranza, Kefa ZhouAbstract:Geochemical anomalies are commonly separated into different geochemical anomaly levels based on one or more thresholds. However, this practice may cause some important geochemical anomaly information to be lost and subsequently draw wrong decisions for mineral exploration. In addition, previous studies indicate that sparse geochemical sampling always entails uncertainty resulting from conventional geochemical interpolation methods because of smoothing effect. Uncertainty can propagate through the various steps of geochemical data analysis that may lead to significant impact on the final results (e.g., anomaly interpretation and mineral exploration). For geochemical anomaly identification, quantifying the probability of unsampled locations and characterizing the spatial uncertainty of geochemical anomaly based on (not) exceeding a key threshold is very important for practical demands such as exploration risk assessment. Considering the limitations of deterministic modeling method and geochemical anomaly assessment, this study proposes a new method of geochemical anomaly uncertainty assessment by combining geostatistical simulation and singularity analysis. A case study for Au anomaly uncertainty assessment is presented in the west Tianshan region (China) so as to verify the feasibility and effectiveness of the proposed method. The sequential Gaussian simulation was adopted to generate a set of equiprobable realizations that were subsequently employed to produce a series of corresponding singularity index realizations by means of singularity analysis. Critical thresholds of E-type singularity index (α) were determined by the method of singularity-Quantile Plot analysis, which were used to simulate the spatial uncertainty of Au anomaly in the study area. The results show that the risk probability of Au anomaly characterized by (not) exceedance of a critical threshold can be considered as an important reference for exploration decision-making and risk management.
T C Chang - One of the best experts on this subject based on the ideXlab platform.
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using normal Quantile Plot to select an appropriate transformation to achieve normality
Computational Statistics & Data Analysis, 2004Co-Authors: W D Tan, F F Gan, T C ChangAbstract:The normal Quantile Plot is a popular and useful tool for assessing the normality of a data set. A nonlinear Plot is used to infer evidence that the data did not come from a normal population. The curvature of a Plot is exploited to suggest a transformation required to normalize the data, if it exists. This is done by comparing the curvature of a Plot against a series of reference curves which correspond to different transformations. Unlike the maximum likelihood method, this technique is robust to outliers.
