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Statistical Interpretation (statistical + interpretation)

Distribution by Scientific Domains

Earth and Environmental Science	37%
Chemistry	25%

Selected Abstracts

Statistical interpretation of NWP products in India

METEOROLOGICAL APPLICATIONS, Issue 1 2002
Parvinder Maini
Although numerical weather prediction (NWP) models provide an objective forecast, poor representation of local topography and other features in these models, necessitates statistical interpretation (SI) of NWP products in terms of local weather. The Perfect Prognostic Method (PPM) is one of the techniques for accomplishing this. At the National Center for Medium Range Weather Forecasting, PPM models for precipitation (quantitative, probability, yes/no) and maximum/minimum temperatures are developed for monsoon season by using analyses from the European Centre for Medium-Range Weather Forecasts. The SI forecast is then obtained by using these PPM models and output from the operational NWP model at the Center. Direct model output (DMO) obtained from the NWP model and the SI forecast are verified against the actual observations. The present study shows the verification scores obtained during the 1997 monsoon season for 10 locations in India. The results show that the SI forecast has good skill and is an improvement over DMO. Copyright © 2002 Royal Meteorological Society. [source]

Optimizing the tuning parameters of least squares support vector machines regression for NIR spectra

JOURNAL OF CHEMOMETRICS, Issue 5 2006
T. Coen
Abstract Partial least squares (PLS) is one of the most used tools in chemometrics. Other data analysis techniques such as artificial neural networks and least squares support vector machines (LS-SVMs) have however made their entry in the field of chemometrics. These techniques can also model nonlinear relations, but the presence of tuning parameters is a serious drawback. These parameters balance the risk of overfitting with the possibility to model the underlying nonlinear relation. In this work a methodology is proposed to initialize and optimize those tuning parameters for LS-SVMs with radial basis function (RBF)-kernel based on a statistical interpretation. In this way, these methods become much more appealing for new users. The presented methods are applied on manure spectra. Although this dataset is only slightly nonlinear, good results were obtained. Copyright © 2007 John Wiley & Sons, Ltd. [source]

Statistical interpretation of NWP products in India

Simultaneous use of serum IgG and IgM for risk scoring of suspected early Lyme borreliosis: graphical and bivariate analyses

APMIS, Issue 4 2010
RAM B. DESSAU
Dessau RB, Ejlertsen T, Hilden J. Simultaneous use of serum IgG and IgM for risk scoring of suspected early Lyme borreliosis: graphical and bivariate analyses. APMIS 2010; 118: 313,23. The laboratory diagnosis of early disseminated Lyme borreliosis (LB) rests on IgM and IgG antibodies in serum. The purpose of this study was to refine the statistical interpretation of IgM and IgG by combining the diagnostic evidence provided by the two immunoglobulins and exploiting the whole range of the quantitative variation in test values. ELISA assays based on purified flagella antigen were performed on sera from 815 healthy Danish blood donors as negative controls and 117 consecutive patients with confirmed neuroborreliosis (NB). A logistic regression model combining the standardized units of the IgM and IgG ELISA assays was constructed and the resulting disease risks graphically evaluated by receiver operating characteristic and ,predictiveness' curves. The combined model improves the discrimination between NB patients and blood donors. Hence, it is possible to report a predicted risk of disease graded for each individual patient, as is theoretically preferable. The predictiveness curve, when adapted to the local pretest probability of LB, allows high-risk and low-risk thresholds to be defined instead of cut-offs based on the laboratory characteristics only, and it allows the extent of under- and over-treatment to be assessed. It is shown that an example patient with low ELISA results in IgM and IgG, considered negative by the conventional cut-off, has a relatively high risk of belonging to the truly diseased population and a low risk of being false positive. Using a 20% high-risk threshold for advising the clinician to consider treatment, the sensitivity of the assay is increased from 76% to 85%, while the specificity is maintained at around 95%. [source]

Trend estimation of financial time series

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 3 2010
Víctor M. Guerrero
Abstract We propose to decompose a financial time series into trend plus noise by means of the exponential smoothing filter. This filter produces statistically efficient estimates of the trend that can be calculated by a straightforward application of the Kalman filter. It can also be interpreted in the context of penalized least squares as a function of a smoothing constant has to be minimized by trading off fitness against smoothness of the trend. The smoothing constant is crucial to decide the degree of smoothness and the problem is how to choose it objectively. We suggest a procedure that allows the user to decide at the outset the desired percentage of smoothness and derive from it the corresponding value of that constant. A definition of smoothness is first proposed as well as an index of relative precision attributable to the smoothing element of the time series. The procedure is extended to series with different frequencies of observation, so that comparable trends can be obtained for say, daily, weekly or intraday observations of the same variable. The theoretical results are derived from an integrated moving average model of order (1, 1) underlying the statistical interpretation of the filter. Expressions of equivalent smoothing constants are derived for series generated by temporal aggregation or systematic sampling of another series. Hence, comparable trend estimates can be obtained for the same time series with different lengths, for different time series of the same length and for series with different frequencies of observation of the same variable. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Skill of statistical interpretation forecasting system during monsoon season in India

ATMOSPHERIC SCIENCE LETTERS, Issue 1 2002
Ashok Kumar
Abstract An evaluation of statistical interpretation(SI) forecast skill for rainfall and maximum/minimum temperature during monsoon season (June,September) is presented. A brief description of the methodology used for SI forecast and data used is also given. The skill of SI forecast is calculated during 1999, monsoon and has been found encouraging. Copyright © 2002 Royal Meteorological Society. [source]

Survey of methodologies for developing media screening values for ecological risk assessment

INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT, Issue 4 2005
Mace G. Barron
Abstract This review evaluates the methodologies of 13 screening value (SV) compilations that have been commonly used in ecological risk assessment (ERA), including compilations from state and U.S. federal agencies, the Oak Ridge National Laboratory (ORNL), Canada, The Netherlands, and Australia. The majority of surfacewater SVs were primarily derived for the protection of aquatic organisms using 2 approaches: (1) a statistical assessment of toxicity values by species groupings, such as "ambient water quality criteria," or (2) extrapolation of a lowest observed adverse effect level determined from limited toxicity data using an uncertainty factor. Sediment SVs were primarily derived for the protection of benthic invertebrates using 2 approaches: (1) statistical interpretations of databases on the incidence of biological effects and chemical concentrations in sediment, or (2) values derived from equilibrium partitioning based on a surfacewater SV. Soil SVs were derived using a diversity of approaches and were usually based on the lowest value determined from soil toxicity to terrestrial plants or invertebrates and, less frequently, from modeled, incidental soil ingestion or chemical accumulation in terrestrial organisms. The various SV compilations and methodologies had varying levels of conservatism and were not consistent in the pathways and receptors considered in the SV derivation. Many SVs were derived from other compilations and were based on outdated values, or they relied on only older toxicity data. Risk assessors involved in ERA should carefully evaluate the technical basis of SVs and consider the uncertainty in any value used to determine the presence or absence of risk and the need for further assessment. [source]

Resolving Paternity Relationships Using X-Chromosome STRs and Bayesian Networks

JOURNAL OF FORENSIC SCIENCES, Issue 4 2007
Didier Hatsch Ph.D.
Abstract:, X-chromosomal short tandem repeats (X-STRs) are very useful in complex paternity cases because they are inherited by male and female offspring in different ways. They complement autosomal STRs (as-STRs) allowing higher paternity probabilities to be attained. These probabilities are expressed in a likelihood ratio (LR). The formulae needed to calculate LR depend on the genotype combinations of suspected pedigrees. LR can also be obtained by the use of Bayesian networks (BNs). These are graphical representations of real situations that can be used to easily calculate complex probabilities. In the present work, two BNs are presented, which are designed to derive LRs for half-sisters/half-sisters and mother/daughter/paternal grandmother relationships. These networks were validated against known formulae and show themselves to be useful in other suspect pedigree situations than those for which they were developed. The BNs were applied in two paternity cases. The application of the mother/daughter/paternal grandmother BN highlighted the complementary value of X-STRs to as-STRs. The same case evaluated without the mother underlined that missing information tends to be conservative if the alleged father is the biological father and otherwise nonconservative. The half-sisters case shows a limitation of statistical interpretations in regard to high allelic frequencies. [source]