scholarly journals Statistical prediction of waterflooding performance by K-means clustering and empirical modeling

2022 ◽  
Author(s):  
Qin-Zhuo Liao ◽  
Liang Xue ◽  
Gang Lei ◽  
Xu Liu ◽  
Shu-Yu Sun ◽  
...  
Keyword(s):  
Empirical Modeling ◽  
Statistical Prediction

scholarly journals Empirical Modeling and Stochastic Simulation of Sea Level Pressure Variability

2016 ◽  
Vol 55 (5) ◽  
pp. 1197-1219 ◽  
Author(s):  
Sergey Kravtsov ◽  
Natalia Tilinina ◽  
Yulia Zyulyaeva ◽  
Sergey K. Gulev
Keyword(s):  
Sea Level ◽  
Diurnal Cycle ◽  
Empirical Model ◽  
Empirical Modeling ◽  
Statistical Prediction ◽  
Main Step ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Statistical Measures ◽  
Wide Range

AbstractThe scope of this work is stochastic emulation of sea level pressure (SLP) for use in error estimation and statistical prediction studies. The input SLP dataset whose statistics are to be emulated was taken from the 1979–2013 ERA-Interim dataset at full 6-hourly temporal and 0.75° spatial resolutions over the Northern Hemisphere. Upon subtracting the monthly climatological mean value and mean diurnal cycle, the SLP anomalies (SLPA) were projected onto the subspace of 1000 leading empirical orthogonal functions of the daily-mean SLPA, which account for the vast majority (>99%) of the full 6-hourly fields’ variance for each season. The main step of this method is the estimation of a linear autoregressive moving-average empirical model for the daily SLPA principal components (PCs) via regularized multiple linear regression; this model was driven, at the stage of simulation, by state-dependent (multiplicative) noise. Last, a diagnostic statistical scheme has been developed and implemented for accurate interpolation of simulated daily SLPA to 6-hourly temporal resolution. Upon transforming the simulated 6-hourly SLPA PCs into the physical space and adding a seasonal climatological mean and mean diurnal cycle, the resulting SLP variability was compared with the actual variability in the ERA-Interim dataset. It is shown that this empirical model produces independent realizations of SLP variability that are nearly indistinguishable from the observed variability over a wide range of statistical measures; these measures include, among others, spatial patterns of bandpass- and low-pass-filtered variability, as well as diverse characteristics of midlatitude cyclone tracks.

Reality versus the statistical rat: Empirical modeling of object investigation

1991 ◽  
Author(s):  
Michael J. Renner ◽  
Charles P. Seltzer
Keyword(s):  
Empirical Modeling

Wildfire Ignitions: A Review of the Science and Recommendations for Empirical Modeling

2013 ◽  
Author(s):  
Jeffrey P. Prestemon ◽  
Todd J. Hawbaker ◽  
Michael Bowden ◽  
John Carpenter ◽  
Maureen T. Brooks ◽  
...  
Keyword(s):  
Empirical Modeling

An evaluation of DSM-5 Section III Personality Disorder Criterion A (Impairment) in accounting for psychopathology

2018 ◽  
Author(s):  
Chelsea Sleep ◽  
Donald Lynam ◽  
Thomas A. Widiger ◽  
Michael L Crowe ◽  
Josh Miller
Keyword(s):  
Personality Disorders ◽  
Discriminant Validity ◽  
Factor Model ◽  
Incremental Validity ◽  
Self Report ◽  
Statistical Prediction ◽  
Dsm 5 ◽  
Criterion A ◽  
Personality Dysfunction ◽  
Axis I

An alternative diagnostic model of personality disorders (AMPD) was introduced in DSM-5 that diagnoses PDs based on the presence of personality impairment (Criterion A) and pathological personality traits (Criterion B). Research examining Criterion A has been limited to date, due to the lack of a specific measure to assess it; this changed, however, with the recent publication of a self-report assessment of personality dysfunction as defined by Criterion A (Levels of Personality Functioning Scale – Self-report; LPFS-SR; Morey, 2017). The aim of the current study was to test several key propositions regarding the role of Criterion A in the AMPD including the underlying factor structure of the LPFS-SR, the discriminant validity of the hypothesized factors, whether Criterion A distinguishes personality psychopathology from Axis I symptoms, the overlap between Criterion A and B, and the incremental predictive utility of Criterion A and B in the statistical prediction of traditional PD symptom counts. Neither a single factor model nor an a priori four-factor model of dysfunction fit the data well. The LPFS-SR dimensions were highly interrelated and manifested little evidence of discriminant validity. In addition, the impairment dimensions manifested robust correlations with measures of both Axis I and II constructs, challenging the notion that personality dysfunction is unique to PDs. Finally, multivariate regression analyses suggested that the traits account for substantially more unique variance in DSM-5 Section II PDs than does personality impairment. These results provide important information as to the functioning of the two main components of the DSM-5 AMPD and raise questions about whether the model may need revision moving forward.Keywords: dysfunction, impairment, personality disorders, Section III, incremental validity Public Significance: The alternative model of personality disorders included in Section III of the 5th addition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) includes two primary components: personality dysfunction and maladaptive traits. The current results raise questions about how a new, DSM-5 aligned measure of personality dysfunction operates with regard its factor structure, discriminant validity, ability to differentiate between personality and non-personality based forms of psychopathology, and incremental validity in the statistical prediction of traditional DSM personality disorders.

Statistical Prediction of Ocean Circulation and Trajectories

2002 ◽  
Author(s):  
Greg Holloway ◽  
William Merryfield
Keyword(s):  
Ocean Circulation ◽  
Statistical Prediction
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