Statistical Prediction Techniques for Analysis of Field Failures

1966 ◽  
Author(s):  
Leonard G. Johnson
Keyword(s):  
Statistical Prediction ◽  
Prediction Techniques

scholarly journals Nonlinear multivariable analysis of SOI and local precipitation and temperature

2005 ◽  
Vol 12 (1) ◽  
pp. 67-74 ◽  
Author(s):  
Y.-H. Jin ◽  
A. Kawamura ◽  
K. Jinno ◽  
R. Berndtsson
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Southern Oscillation ◽  
Statistical Prediction ◽  
Meteorological Variables ◽  
Space Trajectories ◽  
Local Climate ◽  
Prediction Techniques ◽  
Precipitation And Temperature ◽  
Phase Space Trajectories

Abstract. Global climate variability affects important local hydro-meteorological variables like precipitation and temperature. The Southern Oscillation (SO) is an easily quantifiable major driving force that gives impact on regional and local climate. The relationships between SO and local climate variation are, however, characterized by strongly nonlinear processes. Due to this, teleconnections between global-scale hydro-meteorological variables and local climate are not well understood. In this paper, we suggest to study these processes in terms of nonlinear dynamics. Consequently, the nonlinear dynamic relationship between the Southern Oscillation Index (SOI), precipitation, and temperature in Fukuoka, Japan, is investigated using a nonlinear multivariable approach. This approach is based on the joint variation of these variables in the phase space. The joint phase-space variation of SOI, precipitation, and temperature is studied with the primary objective to obtain a better understanding of the dynamical evolution of local hydro-meteorological variables affected by global atmospheric-oceanic phenomena. The results from the analyses display rather clear low-order phase space trajectories when treating the time series individually. However, when plotting phase space trajectories for several time series jointly, complicated higher-order nonlinear relationships emerge between the variables. Consequently, simple data-driven prediction techniques utilizing phase-space characteristics of individual time series may prove successful. On the other hand, since either the time series are too short and/or the phase-space properties are too complex when analysing several variables jointly, it may be difficult to use multivariable statistical prediction techniques for the present investigated variables. In any case, it is essential to further pursue studies regarding links between the SOI and observed local climatic and other geophysical variables even if these links are not fully understood in physical terms.

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.

Dilution studies on three marine outfalls in South Africa

1995 ◽  
Vol 32 (2) ◽  
pp. 297-304
Author(s):  
Willem A. M. Botes ◽  
J. F. Kapp
Keyword(s):  
South Africa ◽  
South African ◽  
Field Data ◽  
Prediction Models ◽  
Feasibility Studies ◽  
The South ◽  
Average Current ◽  
Predicted Values ◽  
Prediction Techniques ◽  
Domestic Effluent

Field dilution studies were conducted on three “deep” water marine outfalls located along the South African coast to establish the comparibility of actual achievable initial dilutions against the theoretical predicted values and, where appropriate, to make recommendations regarding the applicability of the different prediction techniques in the design of future outfalls. The physical processes along the 3000 km long coastline of South Africa are diverse, ranging from dynamic sub-tropical waters on the east coast to cold, stratified stagnant conditions on the west coast. Fourteen existing offshore marine outfalls serve medium to large industries and various local authorities (domestic effluent). For this investigation three outfalls were selected to represent the range of outfall types as well as the diversity of the physical conditions of the South African coastline. The predicted dilutions, using various approaches, compared well with the measured dilutions. It was found that the application of more “simple” prediction techniques (using average current velocities and ambient densities) may be more practical, ensuring a conservative approach, in pre-feasibility studies, compared to the more detailed prediction models, which uses accurate field data (stratification and current profiles), when extensive field data is not readily available.

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