MIRELA SECARĂ
2008-01-01
Tourism represents an important field of economic and social life in our country, and the main sector of the economy of Constanta County is the balneary touristic capitalization of Romanian seaside. In order to statistically analyze hydro tourism on Romanian seaside, we have applied non-parametric methods of measuring and interpretation of existing statistic connections within seaside hydro tourism. Major objective of this research is represented by hydro tourism re-establishment on Romanian ...
Pérez, Hector E; Kettner, Keith
2013-10-01
Time-to-event analysis represents a collection of relatively new, flexible, and robust statistical techniques for investigating the incidence and timing of transitions from one discrete condition to another. Plant biology is replete with examples of such transitions occurring from the cellular to population levels. However, application of these statistical methods has been rare in botanical research. Here, we demonstrate the use of non- and semi-parametric time-to-event and categorical data analyses to address questions regarding seed to seedling transitions of Ipomopsis rubra propagules exposed to various doses of constant or simulated seasonal diel temperatures. Seeds were capable of germinating rapidly to >90 % at 15-25 or 22/11-29/19 °C. Optimum temperatures for germination occurred at 25 or 29/19 °C. Germination was inhibited and seed viability decreased at temperatures ≥30 or 33/24 °C. Kaplan-Meier estimates of survivor functions indicated highly significant differences in temporal germination patterns for seeds exposed to fluctuating or constant temperatures. Extended Cox regression models specified an inverse relationship between temperature and the hazard of germination. Moreover, temperature and the temperature × day interaction had significant effects on germination response. Comparisons to reference temperatures and linear contrasts suggest that summer temperatures (33/24 °C) play a significant role in differential germination responses. Similarly, simple and complex comparisons revealed that the effects of elevated temperatures predominate in terms of components of seed viability. In summary, the application of non- and semi-parametric analyses provides appropriate, powerful data analysis procedures to address various topics in seed biology and more widespread use is encouraged.
Using Mathematica to build Non-parametric Statistical Tables
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Gloria Perez Sainz de Rozas
2003-01-01
Full Text Available In this paper, I present computational procedures to obtian statistical tables. The tables of the asymptotic distribution and the exact distribution of Kolmogorov-Smirnov statistic Dn for one population, the table of the distribution of the runs R, the table of the distribution of Wilcoxon signed-rank statistic W+ and the table of the distribution of Mann-Whitney statistic Ux using Mathematica, Version 3.9 under Window98. I think that it is an interesting cuestion because many statistical packages give the asymptotic significance level in the statistical tests and with these porcedures one can easily calculate the exact significance levels and the left-tail and right-tail probabilities with non-parametric distributions. I have used mathematica to make these calculations because one can use symbolic language to solve recursion relations. It's very easy to generate the format of the tables, and it's possible to obtain any table of the mentioned non-parametric distributions with any precision, not only with the standard parameters more used in Statistics, and without transcription mistakes. Furthermore, using similar procedures, we can generate tables for the following distribution functions: Binomial, Poisson, Hypergeometric, Normal, x2 Chi-Square, T-Student, F-Snedecor, Geometric, Gamma and Beta.
Biological parametric mapping with robust and non-parametric statistics.
Yang, Xue; Beason-Held, Lori; Resnick, Susan M; Landman, Bennett A
2011-07-15
Mapping the quantitative relationship between structure and function in the human brain is an important and challenging problem. Numerous volumetric, surface, regions of interest and voxelwise image processing techniques have been developed to statistically assess potential correlations between imaging and non-imaging metrices. Recently, biological parametric mapping has extended the widely popular statistical parametric mapping approach to enable application of the general linear model to multiple image modalities (both for regressors and regressands) along with scalar valued observations. This approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple imaging modalities. However, as presented, the biological parametric mapping approach is not robust to outliers and may lead to invalid inferences (e.g., artifactual low p-values) due to slight mis-registration or variation in anatomy between subjects. To enable widespread application of this approach, we introduce robust regression and non-parametric regression in the neuroimaging context of application of the general linear model. Through simulation and empirical studies, we demonstrate that our robust approach reduces sensitivity to outliers without substantial degradation in power. The robust approach and associated software package provide a reliable way to quantitatively assess voxelwise correlations between structural and functional neuroimaging modalities. Copyright © 2011 Elsevier Inc. All rights reserved.
Non-parametric versus parametric methods in environmental sciences
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Muhammad Riaz
2016-01-01
Full Text Available This current report intends to highlight the importance of considering background assumptions required for the analysis of real datasets in different disciplines. We will provide comparative discussion of parametric methods (that depends on distributional assumptions (like normality relative to non-parametric methods (that are free from many distributional assumptions. We have chosen a real dataset from environmental sciences (one of the application areas. The findings may be extended to the other disciplines following the same spirit.
Using non-parametric methods in econometric production analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
2012-01-01
by investigating the relationship between the elasticity of scale and the farm size. We use a balanced panel data set of 371~specialised crop farms for the years 2004-2007. A non-parametric specification test shows that neither the Cobb-Douglas function nor the Translog function are consistent with the "true......Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify a functional form of the production function of which the Cobb...... parameter estimates, but also in biased measures which are derived from the parameters, such as elasticities. Therefore, we propose to use non-parametric econometric methods. First, these can be applied to verify the functional form used in parametric production analysis. Second, they can be directly used...
A non-parametric method for correction of global radiation observations
DEFF Research Database (Denmark)
Bacher, Peder; Madsen, Henrik; Perers, Bengt;
2013-01-01
in the observations are corrected. These are errors such as: tilt in the leveling of the sensor, shadowing from surrounding objects, clipping and saturation in the signal processing, and errors from dirt and wear. The method is based on a statistical non-parametric clear-sky model which is applied to both...
Digital spectral analysis parametric, non-parametric and advanced methods
Castanié, Francis
2013-01-01
Digital Spectral Analysis provides a single source that offers complete coverage of the spectral analysis domain. This self-contained work includes details on advanced topics that are usually presented in scattered sources throughout the literature.The theoretical principles necessary for the understanding of spectral analysis are discussed in the first four chapters: fundamentals, digital signal processing, estimation in spectral analysis, and time-series models.An entire chapter is devoted to the non-parametric methods most widely used in industry.High resolution methods a
Using non-parametric methods in econometric production analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
2012-01-01
Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify a functional form of the production function of which the Cobb-Douglas a......Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify a functional form of the production function of which the Cobb...... parameter estimates, but also in biased measures which are derived from the parameters, such as elasticities. Therefore, we propose to use non-parametric econometric methods. First, these can be applied to verify the functional form used in parametric production analysis. Second, they can be directly used...... to estimate production functions without the specification of a functional form. Therefore, they avoid possible misspecification errors due to the use of an unsuitable functional form. In this paper, we use parametric and non-parametric methods to identify the optimal size of Polish crop farms...
Non-parametric Estimation approach in statistical investigation of nuclear spectra
Jafarizadeh, M A; Sabri, H; Maleki, B Rashidian
2011-01-01
In this paper, Kernel Density Estimation (KDE) as a non-parametric estimation method is used to investigate statistical properties of nuclear spectra. The deviation to regular or chaotic dynamics, is exhibited by closer distances to Poisson or Wigner limits respectively which evaluated by Kullback-Leibler Divergence (KLD) measure. Spectral statistics of different sequences prepared by nuclei corresponds to three dynamical symmetry limits of Interaction Boson Model(IBM), oblate and prolate nuclei and also the pairing effect on nuclear level statistics are analyzed (with pure experimental data). KD-based estimated density function, confirm previous predictions with minimum uncertainty (evaluated with Integrate Absolute Error (IAE)) in compare to Maximum Likelihood (ML)-based method. Also, the increasing of regularity degrees of spectra due to pairing effect is reveal.
Using non-parametric methods in econometric production analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
-Douglas function nor the Translog function are consistent with the “true” relationship between the inputs and the output in our data set. We solve this problem by using non-parametric regression. This approach delivers reasonable results, which are on average not too different from the results of the parametric......Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify the functional form of the production function. Most often, the Cobb...... results—including measures that are of interest of applied economists, such as elasticities. Therefore, we propose to use nonparametric econometric methods. First, they can be applied to verify the functional form used in parametric estimations of production functions. Second, they can be directly used...
Non-parametric and least squares Langley plot methods
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P. W. Kiedron
2015-04-01
Full Text Available Langley plots are used to calibrate sun radiometers primarily for the measurement of the aerosol component of the atmosphere that attenuates (scatters and absorbs incoming direct solar radiation. In principle, the calibration of a sun radiometer is a straightforward application of the Bouguer–Lambert–Beer law V=V>/i>0e−τ ·m, where a plot of ln (V voltage vs. m air mass yields a straight line with intercept ln (V0. This ln (V0 subsequently can be used to solve for τ for any measurement of V and calculation of m. This calibration works well on some high mountain sites, but the application of the Langley plot calibration technique is more complicated at other, more interesting, locales. This paper is concerned with ferreting out calibrations at difficult sites and examining and comparing a number of conventional and non-conventional methods for obtaining successful Langley plots. The eleven techniques discussed indicate that both least squares and various non-parametric techniques produce satisfactory calibrations with no significant differences among them when the time series of ln (V0's are smoothed and interpolated with median and mean moving window filters.
COLOR IMAGE RETRIEVAL BASED ON NON-PARAMETRIC STATISTICAL TESTS OF HYPOTHESIS
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R. Shekhar
2016-09-01
Full Text Available A novel method for color image retrieval, based on statistical non-parametric tests such as twosample Wald Test for equality of variance and Man-Whitney U test, is proposed in this paper. The proposed method tests the deviation, i.e. distance in terms of variance between the query and target images; if the images pass the test, then it is proceeded to test the spectrum of energy, i.e. distance between the mean values of the two images; otherwise, the test is dropped. If the query and target images pass the tests then it is inferred that the two images belong to the same class, i.e. both the images are same; otherwise, it is assumed that the images belong to different classes, i.e. both images are different. The proposed method is robust for scaling and rotation, since it adjusts itself and treats either the query image or the target image is the sample of other.
Non-parametric change-point method for differential gene expression detection.
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Yao Wang
Full Text Available BACKGROUND: We proposed a non-parametric method, named Non-Parametric Change Point Statistic (NPCPS for short, by using a single equation for detecting differential gene expression (DGE in microarray data. NPCPS is based on the change point theory to provide effective DGE detecting ability. METHODOLOGY: NPCPS used the data distribution of the normal samples as input, and detects DGE in the cancer samples by locating the change point of gene expression profile. An estimate of the change point position generated by NPCPS enables the identification of the samples containing DGE. Monte Carlo simulation and ROC study were applied to examine the detecting accuracy of NPCPS, and the experiment on real microarray data of breast cancer was carried out to compare NPCPS with other methods. CONCLUSIONS: Simulation study indicated that NPCPS was more effective for detecting DGE in cancer subset compared with five parametric methods and one non-parametric method. When there were more than 8 cancer samples containing DGE, the type I error of NPCPS was below 0.01. Experiment results showed both good accuracy and reliability of NPCPS. Out of the 30 top genes ranked by using NPCPS, 16 genes were reported as relevant to cancer. Correlations between the detecting result of NPCPS and the compared methods were less than 0.05, while between the other methods the values were from 0.20 to 0.84. This indicates that NPCPS is working on different features and thus provides DGE identification from a distinct perspective comparing with the other mean or median based methods.
t-tests, non-parametric tests, and large studies—a paradox of statistical practice?
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Fagerland Morten W
2012-06-01
Full Text Available Abstract Background During the last 30 years, the median sample size of research studies published in high-impact medical journals has increased manyfold, while the use of non-parametric tests has increased at the expense of t-tests. This paper explores this paradoxical practice and illustrates its consequences. Methods A simulation study is used to compare the rejection rates of the Wilcoxon-Mann-Whitney (WMW test and the two-sample t-test for increasing sample size. Samples are drawn from skewed distributions with equal means and medians but with a small difference in spread. A hypothetical case study is used for illustration and motivation. Results The WMW test produces, on average, smaller p-values than the t-test. This discrepancy increases with increasing sample size, skewness, and difference in spread. For heavily skewed data, the proportion of p Conclusions Non-parametric tests are most useful for small studies. Using non-parametric tests in large studies may provide answers to the wrong question, thus confusing readers. For studies with a large sample size, t-tests and their corresponding confidence intervals can and should be used even for heavily skewed data.
Comparison of non-parametric methods for ungrouping coarsely aggregated data
DEFF Research Database (Denmark)
Rizzi, Silvia; Thinggaard, Mikael; Engholm, Gerda
2016-01-01
Background Histograms are a common tool to estimate densities non-parametrically. They are extensively encountered in health sciences to summarize data in a compact format. Examples are age-specific distributions of death or onset of diseases grouped in 5-years age classes with an open-ended age...... methods for ungrouping count data. We compare the performance of two spline interpolation methods, two kernel density estimators and a penalized composite link model first via a simulation study and then with empirical data obtained from the NORDCAN Database. All methods analyzed can be used to estimate...... composite link model performs the best. Conclusion We give an overview and test different methods to estimate detailed distributions from grouped count data. Health researchers can benefit from these versatile methods, which are ready for use in the statistical software R. We recommend using the penalized...
A non-parametric method for correction of global radiation observations
DEFF Research Database (Denmark)
Bacher, Peder; Madsen, Henrik; Perers, Bengt;
2013-01-01
This paper presents a method for correction and alignment of global radiation observations based on information obtained from calculated global radiation, in the present study one-hour forecast of global radiation from a numerical weather prediction (NWP) model is used. Systematical errors detected...... in the observations are corrected. These are errors such as: tilt in the leveling of the sensor, shadowing from surrounding objects, clipping and saturation in the signal processing, and errors from dirt and wear. The method is based on a statistical non-parametric clear-sky model which is applied to both...... University. The method can be useful for optimized use of solar radiation observations for forecasting, monitoring, and modeling of energy production and load which are affected by solar radiation....
The application of non-parametric statistical techniques to an ALARA programme.
Moon, J H; Cho, Y H; Kang, C S
2001-01-01
For the cost-effective reduction of occupational radiation dose (ORD) at nuclear power plants, it is necessary to identify what are the processes of repetitive high ORD during maintenance and repair operations. To identify the processes, the point values such as mean and median are generally used, but they sometimes lead to misjudgment since they cannot show other important characteristics such as dose distributions and frequencies of radiation jobs. As an alternative, the non-parametric analysis method is proposed, which effectively identifies the processes of repetitive high ORD. As a case study, the method is applied to ORD data of maintenance and repair processes at Kori Units 3 and 4 that are pressurised water reactors with 950 MWe capacity and have been operating since 1986 and 1987 respectively, in Korea and the method is demonstrated to be an efficient way of analysing the data.
Comparison of reliability techniques of parametric and non-parametric method
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C. Kalaiselvan
2016-06-01
Full Text Available Reliability of a product or system is the probability that the product performs adequately its intended function for the stated period of time under stated operating conditions. It is function of time. The most widely used nano ceramic capacitor C0G and X7R is used in this reliability study to generate the Time-to failure (TTF data. The time to failure data are identified by Accelerated Life Test (ALT and Highly Accelerated Life Testing (HALT. The test is conducted at high stress level to generate more failure rate within the short interval of time. The reliability method used to convert accelerated to actual condition is Parametric method and Non-Parametric method. In this paper, comparative study has been done for Parametric and Non-Parametric methods to identify the failure data. The Weibull distribution is identified for parametric method; Kaplan–Meier and Simple Actuarial Method are identified for non-parametric method. The time taken to identify the mean time to failure (MTTF in accelerating condition is the same for parametric and non-parametric method with relative deviation.
Using non-parametric methods in econometric production analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify the functional form of the production function. Most often, the Cobb......-Douglas or the Translog production function is used. However, the specification of a functional form for the production function involves the risk of specifying a functional form that is not similar to the “true” relationship between the inputs and the output. This misspecification might result in biased estimation...... results—including measures that are of interest of applied economists, such as elasticities. Therefore, we propose to use nonparametric econometric methods. First, they can be applied to verify the functional form used in parametric estimations of production functions. Second, they can be directly used...
Zoffoli, Luca; Ditroilo, Massimiliano; Federici, Ario; Lucertini, Francesco
2017-09-09
This study used surface electromyography (EMG) to investigate the regions and patterns of activity of the external oblique (EO), erector spinae longissimus (ES), multifidus (MU) and rectus abdominis (RA) muscles during walking (W) and pole walking (PW) performed at different speeds and grades. Eighteen healthy adults undertook W and PW on a motorized treadmill at 60% and 100% of their walk-to-run preferred transition speed at 0% and 7% treadmill grade. The Teager-Kaiser energy operator was employed to improve the muscle activity detection and statistical non-parametric mapping based on paired t-tests was used to highlight statistical differences in the EMG patterns corresponding to different trials. The activation amplitude of all trunk muscles increased at high speed, while no differences were recorded at 7% treadmill grade. ES and MU appeared to support the upper body at the heel-strike during both W and PW, with the latter resulting in elevated recruitment of EO and RA as required to control for the longer stride and the push of the pole. Accordingly, the greater activity of the abdominal muscles and the comparable intervention of the spine extensors supports the use of poles by walkers seeking higher engagement of the lower trunk region. Copyright © 2017 Elsevier Ltd. All rights reserved.
Comparison of non-parametric methods for ungrouping coarsely aggregated data
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Silvia Rizzi
2016-05-01
Full Text Available Abstract Background Histograms are a common tool to estimate densities non-parametrically. They are extensively encountered in health sciences to summarize data in a compact format. Examples are age-specific distributions of death or onset of diseases grouped in 5-years age classes with an open-ended age group at the highest ages. When histogram intervals are too coarse, information is lost and comparison between histograms with different boundaries is arduous. In these cases it is useful to estimate detailed distributions from grouped data. Methods From an extensive literature search we identify five methods for ungrouping count data. We compare the performance of two spline interpolation methods, two kernel density estimators and a penalized composite link model first via a simulation study and then with empirical data obtained from the NORDCAN Database. All methods analyzed can be used to estimate differently shaped distributions; can handle unequal interval length; and allow stretches of 0 counts. Results The methods show similar performance when the grouping scheme is relatively narrow, i.e. 5-years age classes. With coarser age intervals, i.e. in the presence of open-ended age groups, the penalized composite link model performs the best. Conclusion We give an overview and test different methods to estimate detailed distributions from grouped count data. Health researchers can benefit from these versatile methods, which are ready for use in the statistical software R. We recommend using the penalized composite link model when data are grouped in wide age classes.
Trend Analysis of Golestan's Rivers Discharges Using Parametric and Non-parametric Methods
Mosaedi, Abolfazl; Kouhestani, Nasrin
2010-05-01
One of the major problems in human life is climate changes and its problems. Climate changes will cause changes in rivers discharges. The aim of this research is to investigate the trend analysis of seasonal and yearly rivers discharges of Golestan province (Iran). In this research four trend analysis method including, conjunction point, linear regression, Wald-Wolfowitz and Mann-Kendall, for analyzing of river discharges in seasonal and annual periods in significant level of 95% and 99% were applied. First, daily discharge data of 12 hydrometrics stations with a length of 42 years (1965-2007) were selected, after some common statistical tests such as, homogeneity test (by applying G-B and M-W tests), the four mentioned trends analysis tests were applied. Results show that in all stations, for summer data time series, there are decreasing trends with a significant level of 99% according to Mann-Kendall (M-K) test. For autumn time series data, all four methods have similar results. For other periods, the results of these four tests were more or less similar together. While, for some stations the results of tests were different. Keywords: Trend Analysis, Discharge, Non-parametric methods, Wald-Wolfowitz, The Mann-Kendall test, Golestan Province.
A Comparison of Parametric and Non-Parametric Methods Applied to a Likert Scale.
Mircioiu, Constantin; Atkinson, Jeffrey
2017-05-10
A trenchant and passionate dispute over the use of parametric versus non-parametric methods for the analysis of Likert scale ordinal data has raged for the past eight decades. The answer is not a simple "yes" or "no" but is related to hypotheses, objectives, risks, and paradigms. In this paper, we took a pragmatic approach. We applied both types of methods to the analysis of actual Likert data on responses from different professional subgroups of European pharmacists regarding competencies for practice. Results obtained show that with "large" (>15) numbers of responses and similar (but clearly not normal) distributions from different subgroups, parametric and non-parametric analyses give in almost all cases the same significant or non-significant results for inter-subgroup comparisons. Parametric methods were more discriminant in the cases of non-similar conclusions. Considering that the largest differences in opinions occurred in the upper part of the 4-point Likert scale (ranks 3 "very important" and 4 "essential"), a "score analysis" based on this part of the data was undertaken. This transformation of the ordinal Likert data into binary scores produced a graphical representation that was visually easier to understand as differences were accentuated. In conclusion, in this case of Likert ordinal data with high response rates, restraining the analysis to non-parametric methods leads to a loss of information. The addition of parametric methods, graphical analysis, analysis of subsets, and transformation of data leads to more in-depth analyses.
Fujita, André; Takahashi, Daniel Y; Patriota, Alexandre G; Sato, João R
2014-12-10
Statistical inference of functional magnetic resonance imaging (fMRI) data is an important tool in neuroscience investigation. One major hypothesis in neuroscience is that the presence or not of a psychiatric disorder can be explained by the differences in how neurons cluster in the brain. Therefore, it is of interest to verify whether the properties of the clusters change between groups of patients and controls. The usual method to show group differences in brain imaging is to carry out a voxel-wise univariate analysis for a difference between the mean group responses using an appropriate test and to assemble the resulting 'significantly different voxels' into clusters, testing again at cluster level. In this approach, of course, the primary voxel-level test is blind to any cluster structure. Direct assessments of differences between groups at the cluster level seem to be missing in brain imaging. For this reason, we introduce a novel non-parametric statistical test called analysis of cluster structure variability (ANOCVA), which statistically tests whether two or more populations are equally clustered. The proposed method allows us to compare the clustering structure of multiple groups simultaneously and also to identify features that contribute to the differential clustering. We illustrate the performance of ANOCVA through simulations and an application to an fMRI dataset composed of children with attention deficit hyperactivity disorder (ADHD) and controls. Results show that there are several differences in the clustering structure of the brain between them. Furthermore, we identify some brain regions previously not described to be involved in the ADHD pathophysiology, generating new hypotheses to be tested. The proposed method is general enough to be applied to other types of datasets, not limited to fMRI, where comparison of clustering structures is of interest. Copyright © 2014 John Wiley & Sons, Ltd.
A web application for evaluating Phase I methods using a non-parametric optimal benchmark.
Wages, Nolan A; Varhegyi, Nikole
2017-06-01
In evaluating the performance of Phase I dose-finding designs, simulation studies are typically conducted to assess how often a method correctly selects the true maximum tolerated dose under a set of assumed dose-toxicity curves. A necessary component of the evaluation process is to have some concept for how well a design can possibly perform. The notion of an upper bound on the accuracy of maximum tolerated dose selection is often omitted from the simulation study, and the aim of this work is to provide researchers with accessible software to quickly evaluate the operating characteristics of Phase I methods using a benchmark. The non-parametric optimal benchmark is a useful theoretical tool for simulations that can serve as an upper limit for the accuracy of maximum tolerated dose identification based on a binary toxicity endpoint. It offers researchers a sense of the plausibility of a Phase I method's operating characteristics in simulation. We have developed an R shiny web application for simulating the benchmark. The web application has the ability to quickly provide simulation results for the benchmark and requires no programming knowledge. The application is free to access and use on any device with an Internet browser. The application provides the percentage of correct selection of the maximum tolerated dose and an accuracy index, operating characteristics typically used in evaluating the accuracy of dose-finding designs. We hope this software will facilitate the use of the non-parametric optimal benchmark as an evaluation tool in dose-finding simulation.
Non-parametric method for separating domestic hot water heating spikes and space heating
DEFF Research Database (Denmark)
Bacher, Peder; de Saint-Aubain, Philip Anton; Christiansen, Lasse Engbo;
2016-01-01
In this paper a method for separating spikes from a noisy data series, where the data change and evolve over time, is presented. The method is applied on measurements of the total heat load for a single family house. It relies on the fact that the domestic hot water heating is a process generating...... short-lived spikes in the time series, while the space heating changes in slower patterns during the day dependent on the climate and user behavior. The challenge is to separate the domestic hot water heating spikes from the space heating without affecting the natural noise in the space heating...... measurements. The assumption behind the developed method is that the space heating can be estimated by a non-parametric kernel smoother, such that every value significantly above this kernel smoother estimate is identified as a domestic hot water heating spike. First, it is showed how a basic kernel smoothing...
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Alcides Cabrera Campos
2012-09-01
Full Text Available Analyzing data from agricultural pest populations regularly detects that they do not fulfill the theoretical requirements to implement classical ANOVA. Box-Cox transformations and nonparametric statistical methods are commonly used as alternatives to solve this problem. In this paper, we describe the results of applying these techniques to data from Thrips palmi Karny sampled in potato (Solanum tuberosum L. plantations. The X² test was used for the goodness-of-fit of negative binomial distribution and as a test of independence to investigate the relationship between plant strata and insect stages. Seven data transformations were also applied to meet the requirements of classical ANOVA, which failed to eliminate the relationship between mean and variance. Given this negative result, comparisons between insect population densities were made using the nonparametric Kruskal-Wallis ANOVA test. Results from this analysis allowed selecting the insect larval stage and plant middle stratum as keys to design pest sampling plans.Al analizar datos provenientes de poblaciones de plagas agrícolas, regularmente se detecta que no cumplen los requerimientos teóricos para la aplicación del ANDEVA clásico. El uso de transformaciones Box-Cox y de métodos estadísticos no paramétricos resulta la alternativa más utilizada para resolver este inconveniente. En el presente trabajo se exponen los resultados de la aplicación de estas técnicas a datos provenientes de Thrips palmi Karny muestreadas en plantaciones de papa (Solanum tuberosum L. en el período de incidencia de la plaga. Se utilizó la dócima X² para la bondad de ajuste a la distribución binomial negativa y de independencia para investigar la relación entre los estratos de las plantas y los estados del insecto, se aplicaron siete transformaciones a los datos para satisfacer el cumplimiento de los supuestos básicos del ANDEVA, con las cuales no se logró eliminar la relación entre la media y la
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D. Das
2014-04-01
Full Text Available Climate projections simulated by Global Climate Models (GCM are often used for assessing the impacts of climate change. However, the relatively coarse resolutions of GCM outputs often precludes their application towards accurately assessing the effects of climate change on finer regional scale phenomena. Downscaling of climate variables from coarser to finer regional scales using statistical methods are often performed for regional climate projections. Statistical downscaling (SD is based on the understanding that the regional climate is influenced by two factors – the large scale climatic state and the regional or local features. A transfer function approach of SD involves learning a regression model which relates these features (predictors to a climatic variable of interest (predictand based on the past observations. However, often a single regression model is not sufficient to describe complex dynamic relationships between the predictors and predictand. We focus on the covariate selection part of the transfer function approach and propose a nonparametric Bayesian mixture of sparse regression models based on Dirichlet Process (DP, for simultaneous clustering and discovery of covariates within the clusters while automatically finding the number of clusters. Sparse linear models are parsimonious and hence relatively more generalizable than non-sparse alternatives, and lends to domain relevant interpretation. Applications to synthetic data demonstrate the value of the new approach and preliminary results related to feature selection for statistical downscaling shows our method can lead to new insights.
Non-parametric method for measuring gas inhomogeneities from X-ray observations of galaxy clusters
Morandi, Andrea; Cui, Wei
2013-01-01
We present a non-parametric method to measure inhomogeneities in the intracluster medium (ICM) from X-ray observations of galaxy clusters. Analyzing mock Chandra X-ray observations of simulated clusters, we show that our new method enables the accurate recovery of the 3D gas density and gas clumping factor profiles out to large radii of galaxy clusters. We then apply this method to Chandra X-ray observations of Abell 1835 and present the first determination of the gas clumping factor from the X-ray cluster data. We find that the gas clumping factor in Abell 1835 increases with radius and reaches ~2-3 at r=R_{200}. This is in good agreement with the predictions of hydrodynamical simulations, but it is significantly below the values inferred from recent Suzaku observations. We further show that the radially increasing gas clumping factor causes flattening of the derived entropy profile of the ICM and affects physical interpretation of the cluster gas structure, especially at the large cluster-centric radii. Our...
Inferential, non-parametric statistics to assess the quality of probabilistic forecast systems
Maia, A.H.N.; Meinke, H.B.; Lennox, S.; Stone, R.C.
2007-01-01
Many statistical forecast systems are available to interested users. To be useful for decision making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and its statistical manifestation have been firmly established, the forecasts must al
Inferential, non-parametric statistics to assess the quality of probabilistic forecast systems
Maia, A.H.N.; Meinke, H.B.; Lennox, S.; Stone, R.C.
2007-01-01
Many statistical forecast systems are available to interested users. To be useful for decision making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and its statistical manifestation have been firmly established, the forecasts must al
Cabrieto, Jedelyn; Tuerlinckx, Francis; Kuppens, Peter; Grassmann, Mariel; Ceulemans, Eva
2017-06-01
Change point detection in multivariate time series is a complex task since next to the mean, the correlation structure of the monitored variables may also alter when change occurs. DeCon was recently developed to detect such changes in mean and\\or correlation by combining a moving windows approach and robust PCA. However, in the literature, several other methods have been proposed that employ other non-parametric tools: E-divisive, Multirank, and KCP. Since these methods use different statistical approaches, two issues need to be tackled. First, applied researchers may find it hard to appraise the differences between the methods. Second, a direct comparison of the relative performance of all these methods for capturing change points signaling correlation changes is still lacking. Therefore, we present the basic principles behind DeCon, E-divisive, Multirank, and KCP and the corresponding algorithms, to make them more accessible to readers. We further compared their performance through extensive simulations using the settings of Bulteel et al. (Biological Psychology, 98 (1), 29-42, 2014) implying changes in mean and in correlation structure and those of Matteson and James (Journal of the American Statistical Association, 109 (505), 334-345, 2014) implying different numbers of (noise) variables. KCP emerged as the best method in almost all settings. However, in case of more than two noise variables, only DeCon performed adequately in detecting correlation changes.
2016-05-31
Distribution Unlimited UU UU UU UU 31-05-2016 15-Apr-2014 14-Jan-2015 Final Report: Technical Topic 3.2.2.d Bayesian and Non- parametric Statistics...of Papers published in non peer-reviewed journals: Final Report: Technical Topic 3.2.2.d Bayesian and Non- parametric Statistics: Integration of Neural...Transfer N/A Number of graduating undergraduates who achieved a 3.5 GPA to 4.0 (4.0 max scale ): Number of graduating undergraduates funded by a DoD funded
Non-parametric Estimation of Diffusion-Paths Using Wavelet Scaling Methods
DEFF Research Database (Denmark)
Høg, Esben
In continuous time, diffusion processes have been used for modelling financial dynamics for a long time. For example the Ornstein-Uhlenbeck process (the simplest mean-reverting process) has been used to model non-speculative price processes. We discuss non--parametric estimation of these processes...
Non-Parametric Estimation of Diffusion-Paths Using Wavelet Scaling Methods
DEFF Research Database (Denmark)
Høg, Esben
2003-01-01
In continuous time, diffusion processes have been used for modelling financial dynamics for a long time. For example the Ornstein-Uhlenbeck process (the simplest mean--reverting process) has been used to model non-speculative price processes. We discuss non--parametric estimation of these processes...
Non-Parametric Estimation of Diffusion-Paths Using Wavelet Scaling Methods
DEFF Research Database (Denmark)
Høg, Esben
2003-01-01
In continuous time, diffusion processes have been used for modelling financial dynamics for a long time. For example the Ornstein-Uhlenbeck process (the simplest mean--reverting process) has been used to model non-speculative price processes. We discuss non--parametric estimation of these processes...
Non-parametric Estimation of Diffusion-Paths Using Wavelet Scaling Methods
DEFF Research Database (Denmark)
Høg, Esben
In continuous time, diffusion processes have been used for modelling financial dynamics for a long time. For example the Ornstein-Uhlenbeck process (the simplest mean-reverting process) has been used to model non-speculative price processes. We discuss non--parametric estimation of these processes...
Non-parametric group-level statistics for source-resolved ERP analysis.
Lee, Clement; Miyakoshi, Makoto; Delorme, Arnaud; Cauwenberghs, Gert; Makeig, Scott
2015-01-01
We have developed a new statistical framework for group-level event-related potential (ERP) analysis in EEGLAB. The framework calculates the variance of scalp channel signals accounted for by the activity of homogeneous clusters of sources found by independent component analysis (ICA). When ICA data decomposition is performed on each subject's data separately, functionally equivalent ICs can be grouped into EEGLAB clusters. Here, we report a new addition (statPvaf) to the EEGLAB plug-in std_envtopo to enable inferential statistics on main effects and interactions in event related potentials (ERPs) of independent component (IC) processes at the group level. We demonstrate the use of the updated plug-in on simulated and actual EEG data.
A Java program for non-parametric statistic comparison of community structure
Directory of Open Access Journals (Sweden)
WenJun Zhang
2011-09-01
Full Text Available The Java algorithm to statistically compare structure difference of two communities was presented in this study. Euclidean distance, Manhattan distance, Pearson correlation, Point correlation, quadratic correlation and Jaccard coefficient were included in the algorithm. The algorithm was used to compare rice arthropod communities in Pearl River Delta, China, and the results showed that the family composition of arthropods for Guangzhou, Zhongshan, Zhuhai, and Dongguan are not significantly different.
Johnson, H.O.; Gupta, S.C.; Vecchia, A.V.; Zvomuya, F.
2009-01-01
Excessive loading of sediment and nutrients to rivers is a major problem in many parts of the United States. In this study, we tested the non-parametric Seasonal Kendall (SEAKEN) trend model and the parametric USGS Quality of Water trend program (QWTREND) to quantify trends in water quality of the Minnesota River at Fort Snelling from 1976 to 2003. Both methods indicated decreasing trends in flow-adjusted concentrations of total suspended solids (TSS), total phosphorus (TP), and orthophosphorus (OP) and a generally increasing trend in flow-adjusted nitrate plus nitrite-nitrogen (NO3-N) concentration. The SEAKEN results were strongly influenced by the length of the record as well as extreme years (dry or wet) earlier in the record. The QWTREND results, though influenced somewhat by the same factors, were more stable. The magnitudes of trends between the two methods were somewhat different and appeared to be associated with conceptual differences between the flow-adjustment processes used and with data processing methods. The decreasing trends in TSS, TP, and OP concentrations are likely related to conservation measures implemented in the basin. However, dilution effects from wet climate or additional tile drainage cannot be ruled out. The increasing trend in NO3-N concentrations was likely due to increased drainage in the basin. Since the Minnesota River is the main source of sediments to the Mississippi River, this study also addressed the rapid filling of Lake Pepin on the Mississippi River and found the likely cause to be increased flow due to recent wet climate in the region. Copyright ?? 2009 by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. All rights reserved.
Revisiting the Distance Duality Relation using a non-parametric regression method
Rana, Akshay; Jain, Deepak; Mahajan, Shobhit; Mukherjee, Amitabha
2016-07-01
The interdependence of luminosity distance, DL and angular diameter distance, DA given by the distance duality relation (DDR) is very significant in observational cosmology. It is very closely tied with the temperature-redshift relation of Cosmic Microwave Background (CMB) radiation. Any deviation from η(z)≡ DL/DA (1+z)2 =1 indicates a possible emergence of new physics. Our aim in this work is to check the consistency of these relations using a non-parametric regression method namely, LOESS with SIMEX. This technique avoids dependency on the cosmological model and works with a minimal set of assumptions. Further, to analyze the efficiency of the methodology, we simulate a dataset of 020 points of η (z) data based on a phenomenological model η(z)= (1+z)epsilon. The error on the simulated data points is obtained by using the temperature of CMB radiation at various redshifts. For testing the distance duality relation, we use the JLA SNe Ia data for luminosity distances, while the angular diameter distances are obtained from radio galaxies datasets. Since the DDR is linked with CMB temperature-redshift relation, therefore we also use the CMB temperature data to reconstruct η (z). It is important to note that with CMB data, we are able to study the evolution of DDR upto a very high redshift z = 2.418. In this analysis, we find no evidence of deviation from η=1 within a 1σ region in the entire redshift range used in this analysis (0 < z <= 2.418).
Applications of non-parametric statistics and analysis of variance on sample variances
Myers, R. H.
1981-01-01
Nonparametric methods that are available for NASA-type applications are discussed. An attempt will be made here to survey what can be used, to attempt recommendations as to when each would be applicable, and to compare the methods, when possible, with the usual normal-theory procedures that are avavilable for the Gaussion analog. It is important here to point out the hypotheses that are being tested, the assumptions that are being made, and limitations of the nonparametric procedures. The appropriateness of doing analysis of variance on sample variances are also discussed and studied. This procedure is followed in several NASA simulation projects. On the surface this would appear to be reasonably sound procedure. However, difficulties involved center around the normality problem and the basic homogeneous variance assumption that is mase in usual analysis of variance problems. These difficulties discussed and guidelines given for using the methods.
Directory of Open Access Journals (Sweden)
Andrea Furková
2007-06-01
Full Text Available This paper explores the aplication of parametric and non-parametric benchmarking methods in measuring cost efficiency of Slovak and Czech electricity distribution companies. We compare the relative cost efficiency of Slovak and Czech distribution companies using two benchmarking methods: the non-parametric Data Envelopment Analysis (DEA and the Stochastic Frontier Analysis (SFA as the parametric approach. The first part of analysis was based on DEA models. Traditional cross-section CCR and BCC model were modified to cost efficiency estimation. In further analysis we focus on two versions of stochastic frontier cost functioin using panel data: MLE model and GLS model. These models have been applied to an unbalanced panel of 11 (Slovakia 3 and Czech Republic 8 regional electricity distribution utilities over a period from 2000 to 2004. The differences in estimated scores, parameters and ranking of utilities were analyzed. We observed significant differences between parametric methods and DEA approach.
Directory of Open Access Journals (Sweden)
José L. Valencia
2015-11-01
Full Text Available Rainfall, one of the most important climate variables, is commonly studied due to its great heterogeneity, which occasionally causes negative economic, social, and environmental consequences. Modeling the spatial distributions of rainfall patterns over watersheds has become a major challenge for water resources management. Multifractal analysis can be used to reproduce the scale invariance and intermittency of rainfall processes. To identify which factors are the most influential on the variability of multifractal parameters and, consequently, on the spatial distribution of rainfall patterns for different time scales in this study, universal multifractal (UM analysis—C1, α, and γs UM parameters—was combined with non-parametric statistical techniques that allow spatial-temporal comparisons of distributions by gradients. The proposed combined approach was applied to a daily rainfall dataset of 132 time-series from 1931 to 2009, homogeneously spatially-distributed across a 25 km × 25 km grid covering the Ebro River Basin. A homogeneous increase in C1 over the watershed and a decrease in α mainly in the western regions, were detected, suggesting an increase in the frequency of dry periods at different scales and an increase in the occurrence of rainfall process variability over the last decades.
Institute of Scientific and Technical Information of China (English)
范大付; 李春红
2012-01-01
The non-parametric statistics is a test method which does not involve the general parameter and does not depend on the distribution. By using the non-parametric statistics for analyzing and researching the factors of college students＇ math anxiety, we try to solve the negative effect for studying from math anxiety, and increase the academic achievement of the college students.%采用非参数统计方法中的Wilconxon秩和检验、Friedman检验、Mann-WhitneyU检验对大学生数学焦虑的5个主要影响因素进行了定量分析与评价，获得了数学焦虑产生因素的相关非参数统计结果，为解决数学焦虑所带来的学习负效应提供参考。
Directory of Open Access Journals (Sweden)
Andreas H. Melcher
2012-09-01
Full Text Available This study analyses multidimensional spawning habitat suitability of the fish species “Nase” (latin: Chondrostoma nasus. This is the first time non-parametric methods were used to better understand biotic habitat use in theory and practice. In particular, we tested (1 the Decision Tree technique, Chi-squared Automatic Interaction Detectors (CHAID, to identify specific habitat types and (2 Prediction-Configural Frequency Analysis (P-CFA to test for statistical significance. The combination of both non-parametric methods, CHAID and P-CFA, enabled the identification, prediction and interpretation of most typical significant spawning habitats, and we were also able to determine non-typical habitat types, e.g., types in contrast to antitypes. The gradual combination of these two methods underlined three significant habitat types: shaded habitat, fine and coarse substrate habitat depending on high flow velocity. The study affirmed the importance for fish species of shading and riparian vegetation along river banks. In addition, this method provides a weighting of interactions between specific habitat characteristics. The results demonstrate that efficient river restoration requires re-establishing riparian vegetation as well as the open river continuum and hydro-morphological improvements to habitats.
Structuring feature space: a non-parametric method for volumetric transfer function generation.
Maciejewski, Ross; Woo, Insoo; Chen, Wei; Ebert, David S
2009-01-01
The use of multi-dimensional transfer functions for direct volume rendering has been shown to be an effective means of extracting materials and their boundaries for both scalar and multivariate data. The most common multi-dimensional transfer function consists of a two-dimensional (2D) histogram with axes representing a subset of the feature space (e.g., value vs. value gradient magnitude), with each entry in the 2D histogram being the number of voxels at a given feature space pair. Users then assign color and opacity to the voxel distributions within the given feature space through the use of interactive widgets (e.g., box, circular, triangular selection). Unfortunately, such tools lead users through a trial-and-error approach as they assess which data values within the feature space map to a given area of interest within the volumetric space. In this work, we propose the addition of non-parametric clustering within the transfer function feature space in order to extract patterns and guide transfer function generation. We apply a non-parametric kernel density estimation to group voxels of similar features within the 2D histogram. These groups are then binned and colored based on their estimated density, and the user may interactively grow and shrink the binned regions to explore feature boundaries and extract regions of interest. We also extend this scheme to temporal volumetric data in which time steps of 2D histograms are composited into a histogram volume. A three-dimensional (3D) density estimation is then applied, and users can explore regions within the feature space across time without adjusting the transfer function at each time step. Our work enables users to effectively explore the structures found within a feature space of the volume and provide a context in which the user can understand how these structures relate to their volumetric data. We provide tools for enhanced exploration and manipulation of the transfer function, and we show that the initial
Jin, Qian; He, Li-Jun; Zhang, Ai-Bing
2012-01-01
In the recent worldwide campaign for the global biodiversity inventory via DNA barcoding, a simple and easily used measure of confidence for assigning sequences to species in DNA barcoding has not been established so far, although the likelihood ratio test and the bayesian approach had been proposed to address this issue from a statistical point of view. The TDR (Two Dimensional non-parametric Resampling) measure newly proposed in this study offers users a simple and easy approach to evaluate the confidence of species membership in DNA barcoding projects. We assessed the validity and robustness of the TDR approach using datasets simulated under coalescent models, and an empirical dataset, and found that TDR measure is very robust in assessing species membership of DNA barcoding. In contrast to the likelihood ratio test and bayesian approach, the TDR method stands out due to simplicity in both concepts and calculations, with little in the way of restrictive population genetic assumptions. To implement this approach we have developed a computer program package (TDR1.0beta) freely available from ftp://202.204.209.200/education/video/TDR1.0beta.rar.
Scarpazza, Cristina; Nichols, Thomas E; Seramondi, Donato; Maumet, Camille; Sartori, Giuseppe; Mechelli, Andrea
2016-01-01
In recent years, an increasing number of studies have used Voxel Based Morphometry (VBM) to compare a single patient with a psychiatric or neurological condition of interest against a group of healthy controls. However, the validity of this approach critically relies on the assumption that the single patient is drawn from a hypothetical population with a normal distribution and variance equal to that of the control group. In a previous investigation, we demonstrated that family-wise false positive error rate (i.e., the proportion of statistical comparisons yielding at least one false positive) in single case VBM are much higher than expected (Scarpazza et al., 2013). Here, we examine whether the use of non-parametric statistics, which does not rely on the assumptions of normal distribution and equal variance, would enable the investigation of single subjects with good control of false positive risk. We empirically estimated false positive rates (FPRs) in single case non-parametric VBM, by performing 400 statistical comparisons between a single disease-free individual and a group of 100 disease-free controls. The impact of smoothing (4, 8, and 12 mm) and type of pre-processing (Modulated, Unmodulated) was also examined, as these factors have been found to influence FPRs in previous investigations using parametric statistics. The 400 statistical comparisons were repeated using two independent, freely available data sets in order to maximize the generalizability of the results. We found that the family-wise error rate was 5% for increases and 3.6% for decreases in one data set; and 5.6% for increases and 6.3% for decreases in the other data set (5% nominal). Further, these results were not dependent on the level of smoothing and modulation. Therefore, the present study provides empirical evidence that single case VBM studies with non-parametric statistics are not susceptible to high false positive rates. The critical implication of this finding is that VBM can be used
统计软件R在非参数统计教学中的应用%Application of Statistical Software R in the Teaching of Non-Parametric Statistics
Institute of Scientific and Technical Information of China (English)
王志刚; 冯利英; 刘勇
2012-01-01
Introduces the applieation of statistical software R in the teaching of non-parametric statistic's, which is an important branch of statistics. In particular, describes the using of software R in ex- ploratory data analysis, inferential statistics and stochastic, simulation in details. The flexihle, open-sourc, e characteristics of software R makes the data processing more efficient. This soft- ware can realize all the methods of the teaching process, and is convenient fi~r learners to opti- mize and improve based on the previous work. R software is suitable for teaching of the non- parametric statistics.%主要介绍统计软件R在统计中一个重要分支非参数统计中的应用．分别从探索性数据分析、推断统计、随机模拟三个角度介绍R软件的应用。从介绍可以看出R软件的灵活、开源的特性，使得数据处理变得更加高效、得心应手。能够通过软件实现教学环节中的所有方法，并且方便学习者在前人工作基础上对方法进行优化、改进，在非参数统计教学中选用R软件是适合的。
Comparing non-parametric methods for ungrouping coarsely aggregated age-specific distributions
DEFF Research Database (Denmark)
Rizzi, Silvia; Thinggaard, Mikael; Vaupel, James W.
2016-01-01
Demographers have often access to vital statistics that are less than ideal for the purpose of their research. In many instances demographic data are reported in coarse histograms, where the values given are only the summation of true latent values, thereby making detailed analysis troublesome. O...
Out-of-Sample Extensions for Non-Parametric Kernel Methods.
Pan, Binbin; Chen, Wen-Sheng; Chen, Bo; Xu, Chen; Lai, Jianhuang
2017-02-01
Choosing suitable kernels plays an important role in the performance of kernel methods. Recently, a number of studies were devoted to developing nonparametric kernels. Without assuming any parametric form of the target kernel, nonparametric kernel learning offers a flexible scheme to utilize the information of the data, which may potentially characterize the data similarity better. The kernel methods using nonparametric kernels are referred to as nonparametric kernel methods. However, many nonparametric kernel methods are restricted to transductive learning, where the prediction function is defined only over the data points given beforehand. They have no straightforward extension for the out-of-sample data points, and thus cannot be applied to inductive learning. In this paper, we show how to make the nonparametric kernel methods applicable to inductive learning. The key problem of out-of-sample extension is how to extend the nonparametric kernel matrix to the corresponding kernel function. A regression approach in the hyper reproducing kernel Hilbert space is proposed to solve this problem. Empirical results indicate that the out-of-sample performance is comparable to the in-sample performance in most cases. Experiments on face recognition demonstrate the superiority of our nonparametric kernel method over the state-of-the-art parametric kernel methods.
Comparison of non-parametric methods for ungrouping coarsely aggregated data
DEFF Research Database (Denmark)
Rizzi, Silvia; Thinggaard, Mikael; Engholm, Gerda;
2016-01-01
group at the highest ages. When histogram intervals are too coarse, information is lost and comparison between histograms with different boundaries is arduous. In these cases it is useful to estimate detailed distributions from grouped data. Methods From an extensive literature search we identify five...
Energy Technology Data Exchange (ETDEWEB)
Soumia, Sid Ahmed, E-mail: samasoumia@hotmail.fr [Science and Technology Faculty, El Bachir El Ibrahimi University, BordjBouArreridj (Algeria); Messali, Zoubeida, E-mail: messalizoubeida@yahoo.fr [Laboratory of Electrical Engineering(LGE), University of M' sila (Algeria); Ouahabi, Abdeldjalil, E-mail: abdeldjalil.ouahabi@univ-tours.fr [Polytechnic School, University of Tours (EPU - PolytechTours), EPU - Energy and Electronics Department (France); Trepout, Sylvain, E-mail: sylvain.trepout@curie.fr, E-mail: cedric.messaoudi@curie.fr, E-mail: sergio.marco@curie.fr; Messaoudi, Cedric, E-mail: sylvain.trepout@curie.fr, E-mail: cedric.messaoudi@curie.fr, E-mail: sergio.marco@curie.fr; Marco, Sergio, E-mail: sylvain.trepout@curie.fr, E-mail: cedric.messaoudi@curie.fr, E-mail: sergio.marco@curie.fr [INSERMU759, University Campus Orsay, 91405 Orsay Cedex (France)
2015-01-13
The 3D reconstruction of the Cryo-Transmission Electron Microscopy (Cryo-TEM) and Energy Filtering TEM images (EFTEM) hampered by the noisy nature of these images, so that their alignment becomes so difficult. This noise refers to the collision between the frozen hydrated biological samples and the electrons beam, where the specimen is exposed to the radiation with a high exposure time. This sensitivity to the electrons beam led specialists to obtain the specimen projection images at very low exposure time, which resulting the emergence of a new problem, an extremely low signal-to-noise ratio (SNR). This paper investigates the problem of TEM images denoising when they are acquired at very low exposure time. So, our main objective is to enhance the quality of TEM images to improve the alignment process which will in turn improve the three dimensional tomography reconstructions. We have done multiple tests on special TEM images acquired at different exposure time 0.5s, 0.2s, 0.1s and 1s (i.e. with different values of SNR)) and equipped by Golding beads for helping us in the assessment step. We herein, propose a structure to combine multiple noisy copies of the TEM images. The structure is based on four different denoising methods, to combine the multiple noisy TEM images copies. Namely, the four different methods are Soft, the Hard as Wavelet-Thresholding methods, Bilateral Filter as a non-linear technique able to maintain the edges neatly, and the Bayesian approach in the wavelet domain, in which context modeling is used to estimate the parameter for each coefficient. To ensure getting a high signal-to-noise ratio, we have guaranteed that we are using the appropriate wavelet family at the appropriate level. So we have chosen âĂIJsym8âĂİ wavelet at level 3 as the most appropriate parameter. Whereas, for the bilateral filtering many tests are done in order to determine the proper filter parameters represented by the size of the filter, the range parameter and the
Kh., S Rezaei; Hanson, R J; Fouesneau, M
2016-01-01
We present a non-parametric model for inferring the three-dimensional (3D) distribution of dust density in the Milky Way. Our approach uses the extinction measured towards stars at different locations in the Galaxy at approximately known distances. Each extinction measurement is proportional to the integrated dust density along its line-of-sight. Making simple assumptions about the spatial correlation of the dust density, we can infer the most probable 3D distribution of dust across the entire observed region, including along sight lines which were not observed. This is possible because our model employs a Gaussian Process to connect all lines-of-sight. We demonstrate the capability of our model to capture detailed dust density variations using mock data as well as simulated data from the Gaia Universe Model Snapshot. We then apply our method to a sample of giant stars observed by APOGEE and Kepler to construct a 3D dust map over a small region of the Galaxy. Due to our smoothness constraint and its isotropy,...
Heinrich, Lothar; Schmidt, Volker
2012-01-01
We consider spatially homogeneous marked point patterns in an unboundedly expanding convex sampling window. Our main objective is to identify the distribution of the typical mark by constructing an asymptotic \\chi^2-goodness-of-fit test. The corresponding test statistic is based on a natural empirical version of the Palm mark distribution and a smoothed covariance estimator which turns out to be mean-square consistent. Our approach does not require independent marks and allows dependences between the mark field and the point pattern. Instead we impose a suitable \\beta-mixing condition on the underlying stationary marked point process which can be checked for a number of Poisson-based models and, in particular, in the case of geostatistical marking. Our method needs a central limit theorem for \\beta-mixing random fields which is proved by extending Bernstein's blocking technique to non-cubic index sets and seems to be of interest in its own right. By large-scale model-based simulations the performance of our t...
Parametric and Non-Parametric System Modelling
DEFF Research Database (Denmark)
Nielsen, Henrik Aalborg
1999-01-01
considered. It is shown that adaptive estimation in conditional parametric models can be performed by combining the well known methods of local polynomial regression and recursive least squares with exponential forgetting. The approach used for estimation in conditional parametric models also highlights how....... For this purpose non-parametric methods together with additive models are suggested. Also, a new approach specifically designed to detect non-linearities is introduced. Confidence intervals are constructed by use of bootstrapping. As a link between non-parametric and parametric methods a paper dealing with neural...... the focus is on combinations of parametric and non-parametric methods of regression. This combination can be in terms of additive models where e.g. one or more non-parametric term is added to a linear regression model. It can also be in terms of conditional parametric models where the coefficients...
Jongjoo, Kim; Davis, Scott K; Taylor, Jeremy F
2002-06-01
Empirical confidence intervals (CIs) for the estimated quantitative trait locus (QTL) location from selective and non-selective non-parametric bootstrap resampling methods were compared for a genome scan involving an Angus x Brahman reciprocal fullsib backcross population. Genetic maps, based on 357 microsatellite markers, were constructed for 29 chromosomes using CRI-MAP V2.4. Twelve growth, carcass composition and beef quality traits (n = 527-602) were analysed to detect QTLs utilizing (composite) interval mapping approaches. CIs were investigated for 28 likelihood ratio test statistic (LRT) profiles for the one QTL per chromosome model. The CIs from the non-selective bootstrap method were largest (87 7 cM average or 79-2% coverage of test chromosomes). The Selective II procedure produced the smallest CI size (42.3 cM average). However, CI sizes from the Selective II procedure were more variable than those produced by the two LOD drop method. CI ranges from the Selective II procedure were also asymmetrical (relative to the most likely QTL position) due to the bias caused by the tendency for the estimated QTL position to be at a marker position in the bootstrap samples and due to monotonicity and asymmetry of the LRT curve in the original sample.
ANALYSIS OF TIED DATA: AN ALTERNATIVE NON-PARAMETRIC APPROACH
Directory of Open Access Journals (Sweden)
I. C. A. OYEKA
2012-02-01
Full Text Available This paper presents a non-parametric statistical method of analyzing two-sample data that makes provision for the possibility of ties in the data. A test statistic is developed and shown to be free of the effect of any possible ties in the data. An illustrative example is provided and the method is shown to compare favourably with its competitor; the Mann-Whitney test and is more powerful than the latter when there are ties.
Bayesian non parametric modelling of Higgs pair production
Scarpa, Bruno; Dorigo, Tommaso
2017-03-01
Statistical classification models are commonly used to separate a signal from a background. In this talk we face the problem of isolating the signal of Higgs pair production using the decay channel in which each boson decays into a pair of b-quarks. Typically in this context non parametric methods are used, such as Random Forests or different types of boosting tools. We remain in the same non-parametric framework, but we propose to face the problem following a Bayesian approach. A Dirichlet process is used as prior for the random effects in a logit model which is fitted by leveraging the Polya-Gamma data augmentation. Refinements of the model include the insertion in the simple model of P-splines to relate explanatory variables with the response and the use of Bayesian trees (BART) to describe the atoms in the Dirichlet process.
Bayesian non parametric modelling of Higgs pair production
Directory of Open Access Journals (Sweden)
Scarpa Bruno
2017-01-01
Full Text Available Statistical classification models are commonly used to separate a signal from a background. In this talk we face the problem of isolating the signal of Higgs pair production using the decay channel in which each boson decays into a pair of b-quarks. Typically in this context non parametric methods are used, such as Random Forests or different types of boosting tools. We remain in the same non-parametric framework, but we propose to face the problem following a Bayesian approach. A Dirichlet process is used as prior for the random effects in a logit model which is fitted by leveraging the Polya-Gamma data augmentation. Refinements of the model include the insertion in the simple model of P-splines to relate explanatory variables with the response and the use of Bayesian trees (BART to describe the atoms in the Dirichlet process.
Szulc, Stefan
1965-01-01
Statistical Methods provides a discussion of the principles of the organization and technique of research, with emphasis on its application to the problems in social statistics. This book discusses branch statistics, which aims to develop practical ways of collecting and processing numerical data and to adapt general statistical methods to the objectives in a given field.Organized into five parts encompassing 22 chapters, this book begins with an overview of how to organize the collection of such information on individual units, primarily as accomplished by government agencies. This text then
Institute of Scientific and Technical Information of China (English)
张云贵; 赵华; 王丽娜
2012-01-01
To deal with the rising serious information security problem of the industrial control system (ICS) , this paper presents an intrusion detection method of the non-parametric cumulative sum (CUSUM) for industrial control network. Using the output-input dependent characteristics of the ICS, a mathematical model of the ICS is established to predict the output of the system. Once the sensors of the control system are under attack, the actual output will change. At every moment, the difference between the predicted output of the industrial control model and the measured signal by the sensors is calculated, and then the time-based statistical sequence is formed. By the non-parametric CUSUM algorithm, the online detection of the intrusion attacks is implemented and alarmed. The simulated detection experiments show that the proposed method has a good real-time and low false alarm rate. By choosing appropriate parameters r and β of the non-parametric CUSUM algorithm, the intrusion detection method can accurately detect the attacks before substantial damage to the control system and it is also helpful to monitor the misoperation.%为解决日趋严重的工业控制系统(industrial control system,ICS)信息安全问题,提出一种针对工业控制网络的非参数累积和( cumulative sum,CUSUM)入侵检测方法.利用ICS输入决定输出的特性,建立ICS的数学模型预测系统的输出,一旦控制系统的传感器遭受攻击,实际输出信号将发生改变.在每个时刻,计算工业控制模型的预测输出与传感器测量信号的差值,形成基于时间的统计序列,采用非参数CUSUM算法,实现在线检测入侵并报警.仿真检测实验证明,该方法具有良好的实时性和低误报率.选择适当的非参数CUSUM算法参数T和β,该入侵检测方法不但能在攻击对控制系统造成实质伤害前检测出攻击,还对监测ICS中的误操作有一定帮助.
非参数化方法在 DNB 传递分析中的应用%Non-parametric Method Used in DNB Propagation Analysis
Institute of Scientific and Technical Information of China (English)
刘俊强; 黄禹
2014-01-01
Deciding the internal pressure probability distribution of the fuel rod is a fundamental work in the DNB propagation analysis using Monte Carlo method .The traditional parametric method is used to assume that the internal pressure probability of all rods can be characterized by a normal distribution .But this is not always the case , sometimes there is far more differences between normal distribution and the real one . However ,a new method ,the non-parametric method was used in the treatment of the rod internal pressure data because of its applicability anyw here and good precision in the case of large samples ,and the results show that it is more conservative to use non-parametric method than parametric method in DNB propagation analysis .%采用蒙特卡罗方法进行偏离泡核沸腾（DNB）传递分析中一个最基本的工作是确定燃料棒内压的概率分布。通常假设燃料棒的内压服从正态分布即传统的参数化方法。但燃料棒的内压不总是满足正态分布或与正态分布相差较远。为克服这一不足，本工作采用一种新的方法即非参数化的方法计算燃料棒内压的概率分布。通过对压水堆核电厂燃料棒内压数据的非参数化处理，得到燃料棒内压的概率分布并进行DNB传递分析。由计算结果得出：在DNB传递分析中，相较于参数化方法，采用非参数化方法所得的棒内压概率分布具有普遍适用性及大样本下的良好精度，分析结果更为保守、安全。
Freund, Rudolf J; Wilson, William J
2010-01-01
Statistical Methods, 3e provides students with a working introduction to statistical methods offering a wide range of applications that emphasize the quantitative skills useful across many academic disciplines. This text takes a classic approach emphasizing concepts and techniques for working out problems and intepreting results. The book includes research projects, real-world case studies, numerous examples and data exercises organized by level of difficulty. This text requires that a student be familiar with algebra. New to this edition: NEW expansion of exercises a
Non-parametric approach to the study of phenotypic stability.
Ferreira, D F; Fernandes, S B; Bruzi, A T; Ramalho, M A P
2016-02-19
The aim of this study was to undertake the theoretical derivations of non-parametric methods, which use linear regressions based on rank order, for stability analyses. These methods were extension different parametric methods used for stability analyses and the result was compared with a standard non-parametric method. Intensive computational methods (e.g., bootstrap and permutation) were applied, and data from the plant-breeding program of the Biology Department of UFLA (Minas Gerais, Brazil) were used to illustrate and compare the tests. The non-parametric stability methods were effective for the evaluation of phenotypic stability. In the presence of variance heterogeneity, the non-parametric methods exhibited greater power of discrimination when determining the phenotypic stability of genotypes.
Yerlikaya-Özkurt, Fatma; Askan, Aysegul; Weber, Gerhard-Wilhelm
2014-12-01
Ground Motion Prediction Equations (GMPEs) are empirical relationships which are used for determining the peak ground response at a particular distance from an earthquake source. They relate the peak ground responses as a function of earthquake source type, distance from the source, local site conditions where the data are recorded and finally the depth and magnitude of the earthquake. In this article, a new prediction algorithm, called Conic Multivariate Adaptive Regression Splines (CMARS), is employed on an available dataset for deriving a new GMPE. CMARS is based on a special continuous optimization technique, conic quadratic programming. These convex optimization problems are very well-structured, resembling linear programs and, hence, permitting the use of interior point methods. The CMARS method is performed on the strong ground motion database of Turkey. Results are compared with three other GMPEs. CMARS is found to be effective for ground motion prediction purposes.
Non-Parametric Inference in Astrophysics
Wasserman, L H; Nichol, R C; Genovese, C; Jang, W; Connolly, A J; Moore, A W; Schneider, J; Wasserman, Larry; Miller, Christopher J.; Nichol, Robert C.; Genovese, Chris; Jang, Woncheol; Connolly, Andrew J.; Moore, Andrew W.; Schneider, Jeff; group, the PICA
2001-01-01
We discuss non-parametric density estimation and regression for astrophysics problems. In particular, we show how to compute non-parametric confidence intervals for the location and size of peaks of a function. We illustrate these ideas with recent data on the Cosmic Microwave Background. We also briefly discuss non-parametric Bayesian inference.
Non-Parametric Estimation of Correlation Functions
DEFF Research Database (Denmark)
Brincker, Rune; Rytter, Anders; Krenk, Steen
In this paper three methods of non-parametric correlation function estimation are reviewed and evaluated: the direct method, estimation by the Fast Fourier Transform and finally estimation by the Random Decrement technique. The basic ideas of the techniques are reviewed, sources of bias are pointed...... out, and methods to prevent bias are presented. The techniques are evaluated by comparing their speed and accuracy on the simple case of estimating auto-correlation functions for the response of a single degree-of-freedom system loaded with white noise....
Energy Technology Data Exchange (ETDEWEB)
Constantinescu, C C; Yoder, K K; Normandin, M D; Morris, E D [Department of Radiology, Indiana University School of Medicine, Indianapolis, IN (United States); Kareken, D A [Department of Neurology, Indiana University School of Medicine, Indianapolis, IN (United States); Bouman, C A [Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN (United States); O' Connor, S J [Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN (United States)], E-mail: emorris@iupui.edu
2008-03-07
We previously developed a model-independent technique (non-parametric ntPET) for extracting the transient changes in neurotransmitter concentration from paired (rest and activation) PET studies with a receptor ligand. To provide support for our method, we introduced three hypotheses of validation based on work by Endres and Carson (1998 J. Cereb. Blood Flow Metab. 18 1196-210) and Yoder et al (2004 J. Nucl. Med. 45 903-11), and tested them on experimental data. All three hypotheses describe relationships between the estimated free (synaptic) dopamine curves (F{sup DA}(t)) and the change in binding potential ({delta}BP). The veracity of the F{sup DA}(t) curves recovered by nonparametric ntPET is supported when the data adhere to the following hypothesized behaviors: (1) {delta}BP should decline with increasing DA peak time, (2) {delta}BP should increase as the strength of the temporal correlation between F{sup DA}(t) and the free raclopride (F{sup RAC}(t)) curve increases, (3) {delta}BP should decline linearly with the effective weighted availability of the receptor sites. We analyzed regional brain data from 8 healthy subjects who received two [{sup 11}C]raclopride scans: one at rest, and one during which unanticipated IV alcohol was administered to stimulate dopamine release. For several striatal regions, nonparametric ntPET was applied to recover F{sup DA}(t), and binding potential values were determined. Kendall rank-correlation analysis confirmed that the F{sup DA}(t) data followed the expected trends for all three validation hypotheses. Our findings lend credence to our model-independent estimates of F{sup DA}(t). Application of nonparametric ntPET may yield important insights into how alterations in timing of dopaminergic neurotransmission are involved in the pathologies of addiction and other psychiatric disorders.
Korany, Mohamed A; Maher, Hadir M; Galal, Shereen M; Fahmy, Ossama T; Ragab, Marwa A A
2010-11-15
This manuscript discusses the application of chemometrics to the handling of HPLC response data using the internal standard method (ISM). This was performed on a model mixture containing terbutaline sulphate, guaiphenesin, bromhexine HCl, sodium benzoate and propylparaben as an internal standard. Derivative treatment of chromatographic response data of analyte and internal standard was followed by convolution of the resulting derivative curves using 8-points sin x(i) polynomials (discrete Fourier functions). The response of each analyte signal, its corresponding derivative and convoluted derivative data were divided by that of the internal standard to obtain the corresponding ratio data. This was found beneficial in eliminating different types of interferences. It was successfully applied to handle some of the most common chromatographic problems and non-ideal conditions, namely: overlapping chromatographic peaks and very low analyte concentrations. For example, a significant change in the correlation coefficient of sodium benzoate, in case of overlapping peaks, went from 0.9975 to 0.9998 on applying normal conventional peak area and first derivative under Fourier functions methods, respectively. Also a significant improvement in the precision and accuracy for the determination of synthetic mixtures and dosage forms in non-ideal cases was achieved. For example, in the case of overlapping peaks guaiphenesin mean recovery% and RSD% went from 91.57, 9.83 to 100.04, 0.78 on applying normal conventional peak area and first derivative under Fourier functions methods, respectively. This work also compares the application of Theil's method, a non-parametric regression method, in handling the response ratio data, with the least squares parametric regression method, which is considered the de facto standard method used for regression. Theil's method was found to be superior to the method of least squares as it assumes that errors could occur in both x- and y-directions and
Lottery spending: a non-parametric analysis.
Garibaldi, Skip; Frisoli, Kayla; Ke, Li; Lim, Melody
2015-01-01
We analyze the spending of individuals in the United States on lottery tickets in an average month, as reported in surveys. We view these surveys as sampling from an unknown distribution, and we use non-parametric methods to compare properties of this distribution for various demographic groups, as well as claims that some properties of this distribution are constant across surveys. We find that the observed higher spending by Hispanic lottery players can be attributed to differences in education levels, and we dispute previous claims that the top 10% of lottery players consistently account for 50% of lottery sales.
Lottery spending: a non-parametric analysis.
Directory of Open Access Journals (Sweden)
Skip Garibaldi
Full Text Available We analyze the spending of individuals in the United States on lottery tickets in an average month, as reported in surveys. We view these surveys as sampling from an unknown distribution, and we use non-parametric methods to compare properties of this distribution for various demographic groups, as well as claims that some properties of this distribution are constant across surveys. We find that the observed higher spending by Hispanic lottery players can be attributed to differences in education levels, and we dispute previous claims that the top 10% of lottery players consistently account for 50% of lottery sales.
Directory of Open Access Journals (Sweden)
Stochl Jan
2012-06-01
Full Text Available Abstract Background Mokken scaling techniques are a useful tool for researchers who wish to construct unidimensional tests or use questionnaires that comprise multiple binary or polytomous items. The stochastic cumulative scaling model offered by this approach is ideally suited when the intention is to score an underlying latent trait by simple addition of the item response values. In our experience, the Mokken model appears to be less well-known than for example the (related Rasch model, but is seeing increasing use in contemporary clinical research and public health. Mokken's method is a generalisation of Guttman scaling that can assist in the determination of the dimensionality of tests or scales, and enables consideration of reliability, without reliance on Cronbach's alpha. This paper provides a practical guide to the application and interpretation of this non-parametric item response theory method in empirical research with health and well-being questionnaires. Methods Scalability of data from 1 a cross-sectional health survey (the Scottish Health Education Population Survey and 2 a general population birth cohort study (the National Child Development Study illustrate the method and modeling steps for dichotomous and polytomous items respectively. The questionnaire data analyzed comprise responses to the 12 item General Health Questionnaire, under the binary recoding recommended for screening applications, and the ordinal/polytomous responses to the Warwick-Edinburgh Mental Well-being Scale. Results and conclusions After an initial analysis example in which we select items by phrasing (six positive versus six negatively worded items we show that all items from the 12-item General Health Questionnaire (GHQ-12 – when binary scored – were scalable according to the double monotonicity model, in two short scales comprising six items each (Bech’s “well-being” and “distress” clinical scales. An illustration of ordinal item analysis
Stochl, Jan; Jones, Peter B; Croudace, Tim J
2012-06-11
Mokken scaling techniques are a useful tool for researchers who wish to construct unidimensional tests or use questionnaires that comprise multiple binary or polytomous items. The stochastic cumulative scaling model offered by this approach is ideally suited when the intention is to score an underlying latent trait by simple addition of the item response values. In our experience, the Mokken model appears to be less well-known than for example the (related) Rasch model, but is seeing increasing use in contemporary clinical research and public health. Mokken's method is a generalisation of Guttman scaling that can assist in the determination of the dimensionality of tests or scales, and enables consideration of reliability, without reliance on Cronbach's alpha. This paper provides a practical guide to the application and interpretation of this non-parametric item response theory method in empirical research with health and well-being questionnaires. Scalability of data from 1) a cross-sectional health survey (the Scottish Health Education Population Survey) and 2) a general population birth cohort study (the National Child Development Study) illustrate the method and modeling steps for dichotomous and polytomous items respectively. The questionnaire data analyzed comprise responses to the 12 item General Health Questionnaire, under the binary recoding recommended for screening applications, and the ordinal/polytomous responses to the Warwick-Edinburgh Mental Well-being Scale. After an initial analysis example in which we select items by phrasing (six positive versus six negatively worded items) we show that all items from the 12-item General Health Questionnaire (GHQ-12)--when binary scored--were scalable according to the double monotonicity model, in two short scales comprising six items each (Bech's "well-being" and "distress" clinical scales). An illustration of ordinal item analysis confirmed that all 14 positively worded items of the Warwick-Edinburgh Mental
Directory of Open Access Journals (Sweden)
Maria João Nunes
2005-03-01
Full Text Available In atmospheric aerosol sampling, it is inevitable that the air that carries particles is in motion, as a result of both externally driven wind and the sucking action of the sampler itself. High or low air flow sampling speeds may lead to significant particle size bias. The objective of this work is the validation of measurements enabling the comparison of species concentration from both air flow sampling techniques. The presence of several outliers and increase of residuals with concentration becomes obvious, requiring non-parametric methods, recommended for the handling of data which may not be normally distributed. This way, conversion factors are obtained for each of the various species under study using Kendall regression.
Non-parametric estimation of Fisher information from real data
Shemesh, Omri Har; Miñano, Borja; Hoekstra, Alfons G; Sloot, Peter M A
2015-01-01
The Fisher Information matrix is a widely used measure for applications ranging from statistical inference, information geometry, experiment design, to the study of criticality in biological systems. Yet there is no commonly accepted non-parametric algorithm to estimate it from real data. In this rapid communication we show how to accurately estimate the Fisher information in a nonparametric way. We also develop a numerical procedure to minimize the errors by choosing the interval of the finite difference scheme necessary to compute the derivatives in the definition of the Fisher information. Our method uses the recently published "Density Estimation using Field Theory" algorithm to compute the probability density functions for continuous densities. We use the Fisher information of the normal distribution to validate our method and as an example we compute the temperature component of the Fisher Information Matrix in the two dimensional Ising model and show that it obeys the expected relation to the heat capa...
Rawles, Christopher; Thurber, Clifford
2015-08-01
We present a simple, fast, and robust method for automatic detection of P- and S-wave arrivals using a nearest neighbours-based approach. The nearest neighbour algorithm is one of the most popular time-series classification methods in the data mining community and has been applied to time-series problems in many different domains. Specifically, our method is based on the non-parametric time-series classification method developed by Nikolov. Instead of building a model by estimating parameters from the data, the method uses the data itself to define the model. Potential phase arrivals are identified based on their similarity to a set of reference data consisting of positive and negative sets, where the positive set contains examples of analyst identified P- or S-wave onsets and the negative set contains examples that do not contain P waves or S waves. Similarity is defined as the square of the Euclidean distance between vectors representing the scaled absolute values of the amplitudes of the observed signal and a given reference example in time windows of the same length. For both P waves and S waves, a single pass is done through the bandpassed data, producing a score function defined as the ratio of the sum of similarity to positive examples over the sum of similarity to negative examples for each window. A phase arrival is chosen as the centre position of the window that maximizes the score function. The method is tested on two local earthquake data sets, consisting of 98 known events from the Parkfield region in central California and 32 known events from the Alpine Fault region on the South Island of New Zealand. For P-wave picks, using a reference set containing two picks from the Parkfield data set, 98 per cent of Parkfield and 94 per cent of Alpine Fault picks are determined within 0.1 s of the analyst pick. For S-wave picks, 94 per cent and 91 per cent of picks are determined within 0.2 s of the analyst picks for the Parkfield and Alpine Fault data set
Non-parametric partitioning of SAR images
Delyon, G.; Galland, F.; Réfrégier, Ph.
2006-09-01
We describe and analyse a generalization of a parametric segmentation technique adapted to Gamma distributed SAR images to a simple non parametric noise model. The partition is obtained by minimizing the stochastic complexity of a quantized version on Q levels of the SAR image and lead to a criterion without parameters to be tuned by the user. We analyse the reliability of the proposed approach on synthetic images. The quality of the obtained partition will be studied for different possible strategies. In particular, one will discuss the reliability of the proposed optimization procedure. Finally, we will precisely study the performance of the proposed approach in comparison with the statistical parametric technique adapted to Gamma noise. These studies will be led by analyzing the number of misclassified pixels, the standard Hausdorff distance and the number of estimated regions.
DEFF Research Database (Denmark)
Linnet, Kristian
2005-01-01
Bootstrap, HPLC, limit of blank, limit of detection, non-parametric statistics, type I and II errors......Bootstrap, HPLC, limit of blank, limit of detection, non-parametric statistics, type I and II errors...
Directory of Open Access Journals (Sweden)
Kühnast, Corinna
2008-04-01
Full Text Available Background: Although non-normal data are widespread in biomedical research, parametric tests unnecessarily predominate in statistical analyses. Methods: We surveyed five biomedical journals and – for all studies which contain at least the unpaired t-test or the non-parametric Wilcoxon-Mann-Whitney test – investigated the relationship between the choice of a statistical test and other variables such as type of journal, sample size, randomization, sponsoring etc. Results: The non-parametric Wilcoxon-Mann-Whitney was used in 30% of the studies. In a multivariable logistic regression the type of journal, the test object, the scale of measurement and the statistical software were significant. The non-parametric test was more common in case of non-continuous data, in high-impact journals, in studies in humans, and when the statistical software is specified, in particular when SPSS was used.
A non-parametric model for the cosmic velocity field
Branchini, E; Teodoro, L; Frenk, CS; Schmoldt, [No Value; Efstathiou, G; White, SDM; Saunders, W; Sutherland, W; Rowan-Robinson, M; Keeble, O; Tadros, H; Maddox, S; Oliver, S
1999-01-01
We present a self-consistent non-parametric model of the local cosmic velocity field derived from the distribution of IRAS galaxies in the PSCz redshift survey. The survey has been analysed using two independent methods, both based on the assumptions of gravitational instability and linear biasing.
Fresno, Cristóbal; González, Germán Alexis; Merino, Gabriela Alejandra; Flesia, Ana Georgina; Podhajcer, Osvaldo Luis; Llera, Andrea Sabina; Fernández, Elmer Andrés
2017-03-01
The PAM50 classifier is used to assign patients to the highest correlated breast cancer subtype irrespectively of the obtained value. Nonetheless, all subtype correlations are required to build the risk of recurrence (ROR) score, currently used in therapeutic decisions. Present subtype uncertainty estimations are not accurate, seldom considered or require a population-based approach for this context. Here we present a novel single-subject non-parametric uncertainty estimation based on PAM50's gene label permutations. Simulations results ( n = 5228) showed that only 61% subjects can be reliably 'Assigned' to the PAM50 subtype, whereas 33% should be 'Not Assigned' (NA), leaving the rest to tight 'Ambiguous' correlations between subtypes. The NA subjects exclusion from the analysis improved survival subtype curves discrimination yielding a higher proportion of low and high ROR values. Conversely, all NA subjects showed similar survival behaviour regardless of the original PAM50 assignment. We propose to incorporate our PAM50 uncertainty estimation to support therapeutic decisions. Source code can be found in 'pbcmc' R package at Bioconductor. cristobalfresno@gmail.com or efernandez@bdmg.com.ar. Supplementary data are available at Bioinformatics online.
Parametric versus non-parametric simulation
Dupeux, Bérénice; Buysse, Jeroen
2014-01-01
Most of ex-ante impact assessment policy models have been based on a parametric approach. We develop a novel non-parametric approach, called Inverse DEA. We use non parametric efficiency analysis for determining the farm’s technology and behaviour. Then, we compare the parametric approach and the Inverse DEA models to a known data generating process. We use a bio-economic model as a data generating process reflecting a real world situation where often non-linear relationships exist. Results s...
Non-parametric Morphologies of Mergers in the Illustris Simulation
Bignone, Lucas A; Sillero, Emanuel; Pedrosa, Susana E; Pellizza, Leonardo J; Lambas, Diego G
2016-01-01
We study non-parametric morphologies of mergers events in a cosmological context, using the Illustris project. We produce mock g-band images comparable to observational surveys from the publicly available Illustris simulation idealized mock images at $z=0$. We then measure non parametric indicators: asymmetry, Gini, $M_{20}$, clumpiness and concentration for a set of galaxies with $M_* >10^{10}$ M$_\\odot$. We correlate these automatic statistics with the recent merger history of galaxies and with the presence of close companions. Our main contribution is to assess in a cosmological framework, the empirically derived non-parametric demarcation line and average time-scales used to determine the merger rate observationally. We found that 98 per cent of galaxies above the demarcation line have a close companion or have experienced a recent merger event. On average, merger signatures obtained from the $G-M_{20}$ criteria anticorrelate clearly with the elapsing time to the last merger event. We also find that the a...
Ford, Eric B.; Fabrycky, Daniel C.; Steffen, Jason H.; Carter, Joshua A.; Fressin, Francois; Holman, Matthew Jon; Lissauer, Jack J.; Moorhead, Althea V.; Morehead, Robert C.; Ragozzine, Darin; Rowe, Jason F.; Welsh, William F.; Allen, Christopher; Batalha, Natalie M.; Borucki, William J.
2012-01-01
We present a new method for confirming transiting planets based on the combination of transit timingn variations (TTVs) and dynamical stability. Correlated TTVs provide evidence that the pair of bodies are in the same physical system. Orbital stability provides upper limits for the masses of the transiting companions that are in the planetary regime. This paper describes a non-parametric technique for quantifying the statistical significance of TTVs based on the correlation of two TTV data se...
Continuous/discrete non parametric Bayesian belief nets with UNICORN and UNINET
Cooke, R.M.; Kurowicka, D.; Hanea, A.M.; Morales Napoles, O.; Ababei, D.A.; Ale, B.J.M.; Roelen, A.
2007-01-01
Hanea et al. (2006) presented a method for quantifying and computing continuous/discrete non parametric Bayesian Belief Nets (BBN). Influences are represented as conditional rank correlations, and the joint normal copula enables rapid sampling and conditionalization. Further mathematical background
Rohée, E.; Coulon, R.; Carrel, F.; Dautremer, T.; Barat, E.; Montagu, T.; Normand, S.; Jammes, C.
2016-11-01
Radionuclide identification and quantification are a serious concern for many applications as for in situ monitoring at nuclear facilities, laboratory analysis, special nuclear materials detection, environmental monitoring, and waste measurements. High resolution gamma-ray spectrometry based on high purity germanium diode detectors is the best solution available for isotopic identification. Over the last decades, methods have been developed to improve gamma spectra analysis. However, some difficulties remain in the analysis when full energy peaks are folded together with high ratio between their amplitudes, and when the Compton background is much larger compared to the signal of a single peak. In this context, this study deals with the comparison between a conventional analysis based on "iterative peak fitting deconvolution" method and a "nonparametric Bayesian deconvolution" approach developed by the CEA LIST and implemented into the SINBAD code. The iterative peak fit deconvolution is used in this study as a reference method largely validated by industrial standards to unfold complex spectra from HPGe detectors. Complex cases of spectra are studied from IAEA benchmark protocol tests and with measured spectra. The SINBAD code shows promising deconvolution capabilities compared to the conventional method without any expert parameter fine tuning.
Corporate failure: a non parametric method
Directory of Open Access Journals (Sweden)
Ben Jabeur Sami
2013-07-01
Full Text Available A number of authors suggested that the impact of the macroeconomic factors on the incidence of the financial distress, and afterward in case of failure of companies. However, macroeconomic factors rarely, if ever, appear as variables in predictive models that seek to identify distress and failure; modellers generally suggest that the impact of macroeconomic factors has already been taken into account by financial ratio variables. This article presents a systematic study of this domain, by examining the link between the failure of companies and macroeconomic factors for the French companies to identify the most important variables and to estimate their utility in a predictive context. The results of the study suggest that several macroeconomic variables are strictly associated to the failure, and have a predictive value by specifying the relation between the financial distress and the failure.
A non-parametric peak finder algorithm and its application in searches for new physics
Chekanov, S
2011-01-01
We have developed an algorithm for non-parametric fitting and extraction of statistically significant peaks in the presence of statistical and systematic uncertainties. Applications of this algorithm for analysis of high-energy collision data are discussed. In particular, we illustrate how to use this algorithm in general searches for new physics in invariant-mass spectra using pp Monte Carlo simulations.
A non-parametric framework for estimating threshold limit values
Directory of Open Access Journals (Sweden)
Ulm Kurt
2005-11-01
Full Text Available Abstract Background To estimate a threshold limit value for a compound known to have harmful health effects, an 'elbow' threshold model is usually applied. We are interested on non-parametric flexible alternatives. Methods We describe how a step function model fitted by isotonic regression can be used to estimate threshold limit values. This method returns a set of candidate locations, and we discuss two algorithms to select the threshold among them: the reduced isotonic regression and an algorithm considering the closed family of hypotheses. We assess the performance of these two alternative approaches under different scenarios in a simulation study. We illustrate the framework by analysing the data from a study conducted by the German Research Foundation aiming to set a threshold limit value in the exposure to total dust at workplace, as a causal agent for developing chronic bronchitis. Results In the paper we demonstrate the use and the properties of the proposed methodology along with the results from an application. The method appears to detect the threshold with satisfactory success. However, its performance can be compromised by the low power to reject the constant risk assumption when the true dose-response relationship is weak. Conclusion The estimation of thresholds based on isotonic framework is conceptually simple and sufficiently powerful. Given that in threshold value estimation context there is not a gold standard method, the proposed model provides a useful non-parametric alternative to the standard approaches and can corroborate or challenge their findings.
On Parametric (and Non-Parametric Variation
Directory of Open Access Journals (Sweden)
Neil Smith
2009-11-01
Full Text Available This article raises the issue of the correct characterization of ‘Parametric Variation’ in syntax and phonology. After specifying their theoretical commitments, the authors outline the relevant parts of the Principles–and–Parameters framework, and draw a three-way distinction among Universal Principles, Parameters, and Accidents. The core of the contribution then consists of an attempt to provide identity criteria for parametric, as opposed to non-parametric, variation. Parametric choices must be antecedently known, and it is suggested that they must also satisfy seven individually necessary and jointly sufficient criteria. These are that they be cognitively represented, systematic, dependent on the input, deterministic, discrete, mutually exclusive, and irreversible.
Binary Classifier Calibration Using a Bayesian Non-Parametric Approach.
Naeini, Mahdi Pakdaman; Cooper, Gregory F; Hauskrecht, Milos
Learning probabilistic predictive models that are well calibrated is critical for many prediction and decision-making tasks in Data mining. This paper presents two new non-parametric methods for calibrating outputs of binary classification models: a method based on the Bayes optimal selection and a method based on the Bayesian model averaging. The advantage of these methods is that they are independent of the algorithm used to learn a predictive model, and they can be applied in a post-processing step, after the model is learned. This makes them applicable to a wide variety of machine learning models and methods. These calibration methods, as well as other methods, are tested on a variety of datasets in terms of both discrimination and calibration performance. The results show the methods either outperform or are comparable in performance to the state-of-the-art calibration methods.
Statistical methods in astronomy
Long, James P.; de Souza, Rafael S.
2017-01-01
We present a review of data types and statistical methods often encountered in astronomy. The aim is to provide an introduction to statistical applications in astronomy for statisticians and computer scientists. We highlight the complex, often hierarchical, nature of many astronomy inference problems and advocate for cross-disciplinary collaborations to address these challenges.
Statistical Methods for Astronomy
Feigelson, Eric D
2012-01-01
This review outlines concepts of mathematical statistics, elements of probability theory, hypothesis tests and point estimation for use in the analysis of modern astronomical data. Least squares, maximum likelihood, and Bayesian approaches to statistical inference are treated. Resampling methods, particularly the bootstrap, provide valuable procedures when distributions functions of statistics are not known. Several approaches to model selection and good- ness of fit are considered. Applied statistics relevant to astronomical research are briefly discussed: nonparametric methods for use when little is known about the behavior of the astronomical populations or processes; data smoothing with kernel density estimation and nonparametric regression; unsupervised clustering and supervised classification procedures for multivariate problems; survival analysis for astronomical datasets with nondetections; time- and frequency-domain times series analysis for light curves; and spatial statistics to interpret the spati...
Non-parametric star formation histories for 5 dwarf spheroidal galaxies of the local group
Hernández, X; Valls-Gabaud, D; Gilmore, Gerard; Valls-Gabaud, David
2000-01-01
We use recent HST colour-magnitude diagrams of the resolved stellar populations of a sample of local dSph galaxies (Carina, LeoI, LeoII, Ursa Minor and Draco) to infer the star formation histories of these systems, $SFR(t)$. Applying a new variational calculus maximum likelihood method which includes a full Bayesian analysis and allows a non-parametric estimate of the function one is solving for, we infer the star formation histories of the systems studied. This method has the advantage of yielding an objective answer, as one need not assume {\\it a priori} the form of the function one is trying to recover. The results are checked independently using Saha's $W$ statistic. The total luminosities of the systems are used to normalize the results into physical units and derive SN type II rates. We derive the luminosity weighted mean star formation history of this sample of galaxies.
A Non-Parametric Spatial Independence Test Using Symbolic Entropy
Directory of Open Access Journals (Sweden)
López Hernández, Fernando
2008-01-01
Full Text Available In the present paper, we construct a new, simple, consistent and powerful test forspatial independence, called the SG test, by using symbolic dynamics and symbolic entropyas a measure of spatial dependence. We also give a standard asymptotic distribution of anaffine transformation of the symbolic entropy under the null hypothesis of independencein the spatial process. The test statistic and its standard limit distribution, with theproposed symbolization, are invariant to any monotonuous transformation of the data.The test applies to discrete or continuous distributions. Given that the test is based onentropy measures, it avoids smoothed nonparametric estimation. We include a MonteCarlo study of our test, together with the well-known Moran’s I, the SBDS (de Graaffet al, 2001 and (Brett and Pinkse, 1997 non parametric test, in order to illustrate ourapproach.
STATISTICAL METHODS IN HISTORY
Directory of Open Access Journals (Sweden)
Orlov A. I.
2016-01-01
Full Text Available We have given a critical analysis of statistical models and methods for processing text information in historical records to establish the times when there were certain events, ie, to build science-based chronology. There are three main kinds of sources of knowledge of ancient history: ancient texts, the remains of material culture and traditions. The specific date of the extracted by archaeologists objects in most cases can not be found. The group of Academician A.T. Fomenko has developed and applied new statistical methods for analysis of historical texts (Chronicle, based on the intensive use of computer technology. Two major scientific results were: the majority of historical records that we know now, are duplicated (in particular, chronicles, describing the so-called "Ancient Rome" and "Middle Ages", talking about the same events; the known historical chronicles tell us about real events, separated from the present time for not more than 1000 years. It was found that chronicles describing the history of "ancient times" and "Middle Ages" and the chronicle of Chinese history and the history of various European countries do not talk about different, but about the same events. We have the attempt of a new dating of historical events and restoring the true history of human society based on new data. From the standpoint of statistical methods of historical records and images of their fragments – they are special cases of non-numeric objects of nature. Therefore, developed by the group of A.T. Fomenko computer-statistical methods are the part of non-numerical statistics. We have considered some methods of statistical analysis of chronicles applied by the group of A.T. Fomenko: correlation method of maximums; dynasties method; the method of attenuation frequency; questionnaire method codes. New chronology allows us to understand much of the battle of ideas in modern science and mass consciousness. It becomes clear the root cause of cautious
Pataky, Todd C; Vanrenterghem, Jos; Robinson, Mark A
2015-05-01
Biomechanical processes are often manifested as one-dimensional (1D) trajectories. It has been shown that 1D confidence intervals (CIs) are biased when based on 0D statistical procedures, and the non-parametric 1D bootstrap CI has emerged in the Biomechanics literature as a viable solution. The primary purpose of this paper was to clarify that, for 1D biomechanics datasets, the distinction between 0D and 1D methods is much more important than the distinction between parametric and non-parametric procedures. A secondary purpose was to demonstrate that a parametric equivalent to the 1D bootstrap exists in the form of a random field theory (RFT) correction for multiple comparisons. To emphasize these points we analyzed six datasets consisting of force and kinematic trajectories in one-sample, paired, two-sample and regression designs. Results showed, first, that the 1D bootstrap and other 1D non-parametric CIs were qualitatively identical to RFT CIs, and all were very different from 0D CIs. Second, 1D parametric and 1D non-parametric hypothesis testing results were qualitatively identical for all six datasets. Last, we highlight the limitations of 1D CIs by demonstrating that they are complex, design-dependent, and thus non-generalizable. These results suggest that (i) analyses of 1D data based on 0D models of randomness are generally biased unless one explicitly identifies 0D variables before the experiment, and (ii) parametric and non-parametric 1D hypothesis testing provide an unambiguous framework for analysis when one׳s hypothesis explicitly or implicitly pertains to whole 1D trajectories.
Nonparametric statistical methods
Hollander, Myles; Chicken, Eric
2013-01-01
Praise for the Second Edition"This book should be an essential part of the personal library of every practicing statistician."-Technometrics Thoroughly revised and updated, the new edition of Nonparametric Statistical Methods includes additional modern topics and procedures, more practical data sets, and new problems from real-life situations. The book continues to emphasize the importance of nonparametric methods as a significant branch of modern statistics and equips readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for any given sit
Takara, K. T.
2015-12-01
This paper describes a non-parametric frequency analysis method for hydrological extreme-value samples with a size larger than 100, verifying the estimation accuracy with a computer intensive statistics (CIS) resampling such as the bootstrap. Probable maximum values are also incorporated into the analysis for extreme events larger than a design level of flood control. Traditional parametric frequency analysis methods of extreme values include the following steps: Step 1: Collecting and checking extreme-value data; Step 2: Enumerating probability distributions that would be fitted well to the data; Step 3: Parameter estimation; Step 4: Testing goodness of fit; Step 5: Checking the variability of quantile (T-year event) estimates by the jackknife resampling method; and Step_6: Selection of the best distribution (final model). The non-parametric method (NPM) proposed here can skip Steps 2, 3, 4 and 6. Comparing traditional parameter methods (PM) with the NPM, this paper shows that PM often underestimates 100-year quantiles for annual maximum rainfall samples with records of more than 100 years. Overestimation examples are also demonstrated. The bootstrap resampling can do bias correction for the NPM and can also give the estimation accuracy as the bootstrap standard error. This NPM has advantages to avoid various difficulties in above-mentioned steps in the traditional PM. Probable maximum events are also incorporated into the NPM as an upper bound of the hydrological variable. Probable maximum precipitation (PMP) and probable maximum flood (PMF) can be a new parameter value combined with the NPM. An idea how to incorporate these values into frequency analysis is proposed for better management of disasters that exceed the design level. The idea stimulates more integrated approach by geoscientists and statisticians as well as encourages practitioners to consider the worst cases of disasters in their disaster management planning and practices.
Non-parametric seismic hazard analysis in the presence of incomplete data
Yazdani, Azad; Mirzaei, Sajjad; Dadkhah, Koroush
2017-01-01
The distribution of earthquake magnitudes plays a crucial role in the estimation of seismic hazard parameters. Due to the complexity of earthquake magnitude distribution, non-parametric approaches are recommended over classical parametric methods. The main deficiency of the non-parametric approach is the lack of complete magnitude data in almost all cases. This study aims to introduce an imputation procedure for completing earthquake catalog data that will allow the catalog to be used for non-parametric density estimation. Using a Monte Carlo simulation, the efficiency of introduced approach is investigated. This study indicates that when a magnitude catalog is incomplete, the imputation procedure can provide an appropriate tool for seismic hazard assessment. As an illustration, the imputation procedure was applied to estimate earthquake magnitude distribution in Tehran, the capital city of Iran.
Directory of Open Access Journals (Sweden)
Archer Kellie J
2008-02-01
Full Text Available Abstract Background With the popularity of DNA microarray technology, multiple groups of researchers have studied the gene expression of similar biological conditions. Different methods have been developed to integrate the results from various microarray studies, though most of them rely on distributional assumptions, such as the t-statistic based, mixed-effects model, or Bayesian model methods. However, often the sample size for each individual microarray experiment is small. Therefore, in this paper we present a non-parametric meta-analysis approach for combining data from independent microarray studies, and illustrate its application on two independent Affymetrix GeneChip studies that compared the gene expression of biopsies from kidney transplant recipients with chronic allograft nephropathy (CAN to those with normal functioning allograft. Results The simulation study comparing the non-parametric meta-analysis approach to a commonly used t-statistic based approach shows that the non-parametric approach has better sensitivity and specificity. For the application on the two CAN studies, we identified 309 distinct genes that expressed differently in CAN. By applying Fisher's exact test to identify enriched KEGG pathways among those genes called differentially expressed, we found 6 KEGG pathways to be over-represented among the identified genes. We used the expression measurements of the identified genes as predictors to predict the class labels for 6 additional biopsy samples, and the predicted results all conformed to their pathologist diagnosed class labels. Conclusion We present a new approach for combining data from multiple independent microarray studies. This approach is non-parametric and does not rely on any distributional assumptions. The rationale behind the approach is logically intuitive and can be easily understood by researchers not having advanced training in statistics. Some of the identified genes and pathways have been
Kong, Xiangrong; Mas, Valeria; Archer, Kellie J
2008-02-26
With the popularity of DNA microarray technology, multiple groups of researchers have studied the gene expression of similar biological conditions. Different methods have been developed to integrate the results from various microarray studies, though most of them rely on distributional assumptions, such as the t-statistic based, mixed-effects model, or Bayesian model methods. However, often the sample size for each individual microarray experiment is small. Therefore, in this paper we present a non-parametric meta-analysis approach for combining data from independent microarray studies, and illustrate its application on two independent Affymetrix GeneChip studies that compared the gene expression of biopsies from kidney transplant recipients with chronic allograft nephropathy (CAN) to those with normal functioning allograft. The simulation study comparing the non-parametric meta-analysis approach to a commonly used t-statistic based approach shows that the non-parametric approach has better sensitivity and specificity. For the application on the two CAN studies, we identified 309 distinct genes that expressed differently in CAN. By applying Fisher's exact test to identify enriched KEGG pathways among those genes called differentially expressed, we found 6 KEGG pathways to be over-represented among the identified genes. We used the expression measurements of the identified genes as predictors to predict the class labels for 6 additional biopsy samples, and the predicted results all conformed to their pathologist diagnosed class labels. We present a new approach for combining data from multiple independent microarray studies. This approach is non-parametric and does not rely on any distributional assumptions. The rationale behind the approach is logically intuitive and can be easily understood by researchers not having advanced training in statistics. Some of the identified genes and pathways have been reported to be relevant to renal diseases. Further study on the
A non-parametric approach to investigating fish population dynamics
National Research Council Canada - National Science Library
Cook, R.M; Fryer, R.J
2001-01-01
.... Using a non-parametric model for the stock-recruitment relationship it is possible to avoid defining specific functions relating recruitment to stock size while also providing a natural framework to model process error...
Non-parametric system identification from non-linear stochastic response
DEFF Research Database (Denmark)
Rüdinger, Finn; Krenk, Steen
2001-01-01
An estimation method is proposed for identification of non-linear stiffness and damping of single-degree-of-freedom systems under stationary white noise excitation. Non-parametric estimates of the stiffness and damping along with an estimate of the white noise intensity are obtained by suitable p...
Non-parametric three-way mixed ANOVA with aligned rank tests.
Oliver-Rodríguez, Juan C; Wang, X T
2015-02-01
Research problems that require a non-parametric analysis of multifactor designs with repeated measures arise in the behavioural sciences. There is, however, a lack of available procedures in commonly used statistical packages. In the present study, a generalization of the aligned rank test for the two-way interaction is proposed for the analysis of the typical sources of variation in a three-way analysis of variance (ANOVA) with repeated measures. It can be implemented in the usual statistical packages. Its statistical properties are tested by using simulation methods with two sample sizes (n = 30 and n = 10) and three distributions (normal, exponential and double exponential). Results indicate substantial increases in power for non-normal distributions in comparison with the usual parametric tests. Similar levels of Type I error for both parametric and aligned rank ANOVA were obtained with non-normal distributions and large sample sizes. Degrees-of-freedom adjustments for Type I error control in small samples are proposed. The procedure is applied to a case study with 30 participants per group where it detects gender differences in linguistic abilities in blind children not shown previously by other methods.
Statistical methods for forecasting
Abraham, Bovas
2009-01-01
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists."This book, it must be said, lives up to the words on its advertising cover: ''Bridging the gap between introductory, descriptive approaches and highly advanced theoretical treatises, it provides a practical, intermediate level discussion of a variety of forecasting tools, and explains how they relate to one another, both in theory and practice.'' It does just that!"-Journal of the Royal Statistical Society"A well-written work that deals with statistical methods and models that can be used to produce short-term forecasts, this book has wide-ranging applications. It could be used in the context of a study of regression, forecasting, and time series ...
Variable selection in identification of a high dimensional nonlinear non-parametric system
Institute of Scientific and Technical Information of China (English)
Er-Wei BAI; Wenxiao ZHAO; Weixing ZHENG
2015-01-01
The problem of variable selection in system identification of a high dimensional nonlinear non-parametric system is described. The inherent difficulty, the curse of dimensionality, is introduced. Then its connections to various topics and research areas are briefly discussed, including order determination, pattern recognition, data mining, machine learning, statistical regression and manifold embedding. Finally, some results of variable selection in system identification in the recent literature are presented.
DEFF Research Database (Denmark)
Henningsen, Geraldine; Henningsen, Arne; Henning, Christian H. C. A.
All business transactions as well as achieving innovations take up resources, subsumed under the concept of transaction costs (TAC). One of the major factors in TAC theory is information. Information networks can catalyse the interpersonal information exchange and hence, increase the access to no...... are unveiled by reduced productivity. A cross-validated local linear non-parametric regression shows that good information networks increase the productivity of farms. A bootstrapping procedure confirms that this result is statistically significant....
Further Research into a Non-Parametric Statistical Screening System.
1979-12-14
Let X = V if birth weight is high X2 = 0 if gestation length is short V2 if gestation length is long Normal babies have high birth weight and long... gestation length or low birth weight and short gestation length . Abnormal babies have either of the other two combinations ((0, 1) or (1, 0)). The LDF
Non-Parametric Bayesian Areal Linguistics
Daumé, Hal
2009-01-01
We describe a statistical model over linguistic areas and phylogeny. Our model recovers known areas and identifies a plausible hierarchy of areal features. The use of areas improves genetic reconstruction of languages both qualitatively and quantitatively according to a variety of metrics. We model linguistic areas by a Pitman-Yor process and linguistic phylogeny by Kingman's coalescent.
A New Non-Parametric Approach to Galaxy Morphological Classification
Lotz, J M; Madau, P; Lotz, Jennifer M.; Primack, Joel; Madau, Piero
2003-01-01
We present two new non-parametric methods for quantifying galaxy morphology: the relative distribution of the galaxy pixel flux values (the Gini coefficient or G) and the second-order moment of the brightest 20% of the galaxy's flux (M20). We test the robustness of G and M20 to decreasing signal-to-noise and spatial resolution, and find that both measures are reliable to within 10% at average signal-to-noise per pixel greater than 3 and resolutions better than 1000 pc and 500 pc, respectively. We have measured G and M20, as well as concentration (C), asymmetry (A), and clumpiness (S) in the rest-frame near-ultraviolet/optical wavelengths for 150 bright local "normal" Hubble type galaxies (E-Sd) galaxies and 104 0.05 < z < 0.25 ultra-luminous infrared galaxies (ULIRGs).We find that most local galaxies follow a tight sequence in G-M20-C, where early-types have high G and C and low M20 and late-type spirals have lower G and C and higher M20. The majority of ULIRGs lie above the normal galaxy G-M20 sequence...
Non-Parametric Tests of Structure for High Angular Resolution Diffusion Imaging in Q-Space
Olhede, Sofia C
2010-01-01
High angular resolution diffusion imaging data is the observed characteristic function for the local diffusion of water molecules in tissue. This data is used to infer structural information in brain imaging. Non-parametric scalar measures are proposed to summarize such data, and to locally characterize spatial features of the diffusion probability density function (PDF), relying on the geometry of the characteristic function. Summary statistics are defined so that their distributions are, to first order, both independent of nuisance parameters and also analytically tractable. The dominant direction of the diffusion at a spatial location (voxel) is determined, and a new set of axes are introduced in Fourier space. Variation quantified in these axes determines the local spatial properties of the diffusion density. Non-parametric hypothesis tests for determining whether the diffusion is unimodal, isotropic or multi-modal are proposed. More subtle characteristics of white-matter microstructure, such as the degre...
Ford, Eric B; Steffen, Jason H; Carter, Joshua A; Fressin, Francois; Holman, Matthew J; Lissauer, Jack J; Moorhead, Althea V; Morehead, Robert C; Ragozzine, Darin; Rowe, Jason F; Welsh, William F; Allen, Christopher; Batalha, Natalie M; Borucki, William J; Bryson, Stephen T; Buchhave, Lars A; Burke, Christopher J; Caldwell, Douglas A; Charbonneau, David; Clarke, Bruce D; Cochran, William D; Désert, Jean-Michel; Endl, Michael; Everett, Mark E; Fischer, Debra A; Gautier, Thomas N; Gilliland, Ron L; Jenkins, Jon M; Haas, Michael R; Horch, Elliott; Howell, Steve B; Ibrahim, Khadeejah A; Isaacson, Howard; Koch, David G; Latham, David W; Li, Jie; Lucas, Philip; MacQueen, Phillip J; Marcy, Geoffrey W; McCauliff, Sean; Mullally, Fergal R; Quinn, Samuel N; Quintana, Elisa; Shporer, Avi; Still, Martin; Tenenbaum, Peter; Thompson, Susan E; Torres, Guillermo; Twicken, Joseph D; Wohler, Bill
2012-01-01
We present a new method for confirming transiting planets based on the combination of transit timingn variations (TTVs) and dynamical stability. Correlated TTVs provide evidence that the pair of bodies are in the same physical system. Orbital stability provides upper limits for the masses of the transiting companions that are in the planetary regime. This paper describes a non-parametric technique for quantifying the statistical significance of TTVs based on the correlation of two TTV data sets. We apply this method to an analysis of the transit timing variations of two stars with multiple transiting planet candidates identified by Kepler. We confirm four transiting planets in two multiple planet systems based on their TTVs and the constraints imposed by dynamical stability. An additional three candidates in these same systems are not confirmed as planets, but are likely to be validated as real planets once further observations and analyses are possible. If all were confirmed, these systems would be near 4:6:...
A Non Parametric Study of the Volatility of the Economy as a Country Risk Predictor
Costanzo, Sabatino; Dominguez, Ramses; Moreno, William
2007-01-01
This paper intends to explain Venezuela's country spread behavior through the Neural Networks analysis of a monthly economic activity general index of economic indicators constructed by the Central Bank of Venezuela, a measure of the shocks affecting country risk of emerging markets and the U.S. short term interest rate. The use of non parametric methods allowed the finding of non linear relationship between these inputs and the country risk. The networks performance was evaluated using the method of excess predictability.
Non-parametric combination and related permutation tests for neuroimaging.
Winkler, Anderson M; Webster, Matthew A; Brooks, Jonathan C; Tracey, Irene; Smith, Stephen M; Nichols, Thomas E
2016-04-01
In this work, we show how permutation methods can be applied to combination analyses such as those that include multiple imaging modalities, multiple data acquisitions of the same modality, or simply multiple hypotheses on the same data. Using the well-known definition of union-intersection tests and closed testing procedures, we use synchronized permutations to correct for such multiplicity of tests, allowing flexibility to integrate imaging data with different spatial resolutions, surface and/or volume-based representations of the brain, including non-imaging data. For the problem of joint inference, we propose and evaluate a modification of the recently introduced non-parametric combination (NPC) methodology, such that instead of a two-phase algorithm and large data storage requirements, the inference can be performed in a single phase, with reasonable computational demands. The method compares favorably to classical multivariate tests (such as MANCOVA), even when the latter is assessed using permutations. We also evaluate, in the context of permutation tests, various combining methods that have been proposed in the past decades, and identify those that provide the best control over error rate and power across a range of situations. We show that one of these, the method of Tippett, provides a link between correction for the multiplicity of tests and their combination. Finally, we discuss how the correction can solve certain problems of multiple comparisons in one-way ANOVA designs, and how the combination is distinguished from conjunctions, even though both can be assessed using permutation tests. We also provide a common algorithm that accommodates combination and correction.
Non-parametric analysis of rating transition and default data
DEFF Research Database (Denmark)
Fledelius, Peter; Lando, David; Perch Nielsen, Jens
2004-01-01
We demonstrate the use of non-parametric intensity estimation - including construction of pointwise confidence sets - for analyzing rating transition data. We find that transition intensities away from the class studied here for illustration strongly depend on the direction of the previous move b...... but that this dependence vanishes after 2-3 years....
Non-parametric Bayesian inference for inhomogeneous Markov point processes
DEFF Research Database (Denmark)
Berthelsen, Kasper Klitgaard; Møller, Jesper
With reference to a specific data set, we consider how to perform a flexible non-parametric Bayesian analysis of an inhomogeneous point pattern modelled by a Markov point process, with a location dependent first order term and pairwise interaction only. A priori we assume that the first order term...
Non-parametric analysis of rating transition and default data
DEFF Research Database (Denmark)
Fledelius, Peter; Lando, David; Perch Nielsen, Jens
2004-01-01
We demonstrate the use of non-parametric intensity estimation - including construction of pointwise confidence sets - for analyzing rating transition data. We find that transition intensities away from the class studied here for illustration strongly depend on the direction of the previous move...
Directory of Open Access Journals (Sweden)
Sergio A. Alvarado
2010-12-01
Full Text Available Objetivo: Evaluar la eficiencia predictiva de modelos estadísticos paramétricos y no paramétricos para predecir episodios críticos de contaminación por material particulado PM10 del día siguiente, que superen en Santiago de Chile la norma de calidad diaria. Una predicción adecuada de tales episodios permite a la autoridad decretar medidas restrictivas que aminoren la gravedad del episodio, y consecuentemente proteger la salud de la comunidad. Método: Se trabajó con las concentraciones de material particulado PM10 registradas en una estación asociada a la red de monitorización de la calidad del aire MACAM-2, considerando 152 observaciones diarias de 14 variables, y con información meteorológica registrada durante los años 2001 a 2004. Se ajustaron modelos estadísticos paramétricos Gamma usando el paquete estadístico STATA v11, y no paramétricos usando una demo del software estadístico MARS v 2.0 distribuida por Salford-Systems. Resultados: Ambos métodos de modelación presentan una alta correlación entre los valores observados y los predichos. Los modelos Gamma presentan mejores aciertos que MARS para las concentraciones de PM10 con valores Objective: To evaluate the predictive efficiency of two statistical models (one parametric and the other non-parametric to predict critical episodes of air pollution exceeding daily air quality standards in Santiago, Chile by using the next day PM10 maximum 24h value. Accurate prediction of such episodes would allow restrictive measures to be applied by health authorities to reduce their seriousness and protect the community´s health. Methods: We used the PM10 concentrations registered by a station of the Air Quality Monitoring Network (152 daily observations of 14 variables and meteorological information gathered from 2001 to 2004. To construct predictive models, we fitted a parametric Gamma model using STATA v11 software and a non-parametric MARS model by using a demo version of Salford
Non-parametric Bayesian human motion recognition using a single MEMS tri-axial accelerometer.
Ahmed, M Ejaz; Song, Ju Bin
2012-09-27
In this paper, we propose a non-parametric clustering method to recognize the number of human motions using features which are obtained from a single microelectromechanical system (MEMS) accelerometer. Since the number of human motions under consideration is not known a priori and because of the unsupervised nature of the proposed technique, there is no need to collect training data for the human motions. The infinite Gaussian mixture model (IGMM) and collapsed Gibbs sampler are adopted to cluster the human motions using extracted features. From the experimental results, we show that the unanticipated human motions are detected and recognized with significant accuracy, as compared with the parametric Fuzzy C-Mean (FCM) technique, the unsupervised K-means algorithm, and the non-parametric mean-shift method.
Non-Parametric Bayesian Human Motion Recognition Using a Single MEMS Tri-Axial Accelerometer
Directory of Open Access Journals (Sweden)
M. Ejaz Ahmed
2012-09-01
Full Text Available In this paper, we propose a non-parametric clustering method to recognize the number of human motions using features which are obtained from a single microelectromechanical system (MEMS accelerometer. Since the number of human motions under consideration is not known a priori and because of the unsupervised nature of the proposed technique, there is no need to collect training data for the human motions. The infinite Gaussian mixture model (IGMM and collapsed Gibbs sampler are adopted to cluster the human motions using extracted features. From the experimental results, we show that the unanticipated human motions are detected and recognized with significant accuracy, as compared with the parametric Fuzzy C-Mean (FCM technique, the unsupervised K-means algorithm, and the non-parametric mean-shift method.
Parametric and Non-Parametric System Modelling
DEFF Research Database (Denmark)
Nielsen, Henrik Aalborg
1999-01-01
other aspects, the properties of a method for parameter estimation in stochastic differential equations is considered within the field of heat dynamics of buildings. In the second paper a lack-of-fit test for stochastic differential equations is presented. The test can be applied to both linear and non-linear...... networks is included. In this paper, neural networks are used for predicting the electricity production of a wind farm. The results are compared with results obtained using an adaptively estimated ARX-model. Finally, two papers on stochastic differential equations are included. In the first paper, among...... stochastic differential equations. Some applications are presented in the papers. In the summary report references are made to a number of other applications. Resumé på dansk: Nærværende afhandling består af ti artikler publiceret i perioden 1996-1999 samt et sammendrag og en perspektivering heraf. I...
Statistical methods in nonlinear dynamics
Indian Academy of Sciences (India)
K P N Murthy; R Harish; S V M Satyanarayana
2005-03-01
Sensitivity to initial conditions in nonlinear dynamical systems leads to exponential divergence of trajectories that are initially arbitrarily close, and hence to unpredictability. Statistical methods have been found to be helpful in extracting useful information about such systems. In this paper, we review briefly some statistical methods employed in the study of deterministic and stochastic dynamical systems. These include power spectral analysis and aliasing, extreme value statistics and order statistics, recurrence time statistics, the characterization of intermittency in the Sinai disorder problem, random walk analysis of diffusion in the chaotic pendulum, and long-range correlations in stochastic sequences of symbols.
Methods of statistical model estimation
Hilbe, Joseph
2013-01-01
Methods of Statistical Model Estimation examines the most important and popular methods used to estimate parameters for statistical models and provide informative model summary statistics. Designed for R users, the book is also ideal for anyone wanting to better understand the algorithms used for statistical model fitting. The text presents algorithms for the estimation of a variety of regression procedures using maximum likelihood estimation, iteratively reweighted least squares regression, the EM algorithm, and MCMC sampling. Fully developed, working R code is constructed for each method. Th
Non-parametric foreground subtraction for 21cm epoch of reionization experiments
Harker, Geraint; Bernardi, Gianni; Brentjens, Michiel A; De Bruyn, A G; Ciardi, Benedetta; Jelic, Vibor; Koopmans, Leon V E; Labropoulos, Panagiotis; Mellema, Garrelt; Offringa, Andre; Pandey, V N; Schaye, Joop; Thomas, Rajat M; Yatawatta, Sarod
2009-01-01
An obstacle to the detection of redshifted 21cm emission from the epoch of reionization (EoR) is the presence of foregrounds which exceed the cosmological signal in intensity by orders of magnitude. We argue that in principle it would be better to fit the foregrounds non-parametrically - allowing the data to determine their shape - rather than selecting some functional form in advance and then fitting its parameters. Non-parametric fits often suffer from other problems, however. We discuss these before suggesting a non-parametric method, Wp smoothing, which seems to avoid some of them. After outlining the principles of Wp smoothing we describe an algorithm used to implement it. We then apply Wp smoothing to a synthetic data cube for the LOFAR EoR experiment. The performance of Wp smoothing, measured by the extent to which it is able to recover the variance of the cosmological signal and to which it avoids leakage of power from the foregrounds, is compared to that of a parametric fit, and to another non-parame...
Non-parametric Tuning of PID Controllers A Modified Relay-Feedback-Test Approach
Boiko, Igor
2013-01-01
The relay feedback test (RFT) has become a popular and efficient tool used in process identification and automatic controller tuning. Non-parametric Tuning of PID Controllers couples new modifications of classical RFT with application-specific optimal tuning rules to form a non-parametric method of test-and-tuning. Test and tuning are coordinated through a set of common parameters so that a PID controller can obtain the desired gain or phase margins in a system exactly, even with unknown process dynamics. The concept of process-specific optimal tuning rules in the nonparametric setup, with corresponding tuning rules for flow, level pressure, and temperature control loops is presented in the text. Common problems of tuning accuracy based on parametric and non-parametric approaches are addressed. In addition, the text treats the parametric approach to tuning based on the modified RFT approach and the exact model of oscillations in the system under test using the locus of a perturbedrelay system (LPRS) meth...
Statistical methods for ranking data
Alvo, Mayer
2014-01-01
This book introduces advanced undergraduate, graduate students and practitioners to statistical methods for ranking data. An important aspect of nonparametric statistics is oriented towards the use of ranking data. Rank correlation is defined through the notion of distance functions and the notion of compatibility is introduced to deal with incomplete data. Ranking data are also modeled using a variety of modern tools such as CART, MCMC, EM algorithm and factor analysis. This book deals with statistical methods used for analyzing such data and provides a novel and unifying approach for hypotheses testing. The techniques described in the book are illustrated with examples and the statistical software is provided on the authors’ website.
A non-parametric peak calling algorithm for DamID-Seq.
Directory of Open Access Journals (Sweden)
Renhua Li
Full Text Available Protein-DNA interactions play a significant role in gene regulation and expression. In order to identify transcription factor binding sites (TFBS of double sex (DSX-an important transcription factor in sex determination, we applied the DNA adenine methylation identification (DamID technology to the fat body tissue of Drosophila, followed by deep sequencing (DamID-Seq. One feature of DamID-Seq data is that induced adenine methylation signals are not assured to be symmetrically distributed at TFBS, which renders the existing peak calling algorithms for ChIP-Seq, including SPP and MACS, inappropriate for DamID-Seq data. This challenged us to develop a new algorithm for peak calling. A challenge in peaking calling based on sequence data is estimating the averaged behavior of background signals. We applied a bootstrap resampling method to short sequence reads in the control (Dam only. After data quality check and mapping reads to a reference genome, the peaking calling procedure compromises the following steps: 1 reads resampling; 2 reads scaling (normalization and computing signal-to-noise fold changes; 3 filtering; 4 Calling peaks based on a statistically significant threshold. This is a non-parametric method for peak calling (NPPC. We also used irreproducible discovery rate (IDR analysis, as well as ChIP-Seq data to compare the peaks called by the NPPC. We identified approximately 6,000 peaks for DSX, which point to 1,225 genes related to the fat body tissue difference between female and male Drosophila. Statistical evidence from IDR analysis indicated that these peaks are reproducible across biological replicates. In addition, these peaks are comparable to those identified by use of ChIP-Seq on S2 cells, in terms of peak number, location, and peaks width.
A non-parametric peak calling algorithm for DamID-Seq.
Li, Renhua; Hempel, Leonie U; Jiang, Tingbo
2015-01-01
Protein-DNA interactions play a significant role in gene regulation and expression. In order to identify transcription factor binding sites (TFBS) of double sex (DSX)-an important transcription factor in sex determination, we applied the DNA adenine methylation identification (DamID) technology to the fat body tissue of Drosophila, followed by deep sequencing (DamID-Seq). One feature of DamID-Seq data is that induced adenine methylation signals are not assured to be symmetrically distributed at TFBS, which renders the existing peak calling algorithms for ChIP-Seq, including SPP and MACS, inappropriate for DamID-Seq data. This challenged us to develop a new algorithm for peak calling. A challenge in peaking calling based on sequence data is estimating the averaged behavior of background signals. We applied a bootstrap resampling method to short sequence reads in the control (Dam only). After data quality check and mapping reads to a reference genome, the peaking calling procedure compromises the following steps: 1) reads resampling; 2) reads scaling (normalization) and computing signal-to-noise fold changes; 3) filtering; 4) Calling peaks based on a statistically significant threshold. This is a non-parametric method for peak calling (NPPC). We also used irreproducible discovery rate (IDR) analysis, as well as ChIP-Seq data to compare the peaks called by the NPPC. We identified approximately 6,000 peaks for DSX, which point to 1,225 genes related to the fat body tissue difference between female and male Drosophila. Statistical evidence from IDR analysis indicated that these peaks are reproducible across biological replicates. In addition, these peaks are comparable to those identified by use of ChIP-Seq on S2 cells, in terms of peak number, location, and peaks width.
Statistical Methods in Integrative Genomics
Richardson, Sylvia; Tseng, George C.; Sun, Wei
2016-01-01
Statistical methods in integrative genomics aim to answer important biology questions by jointly analyzing multiple types of genomic data (vertical integration) or aggregating the same type of data across multiple studies (horizontal integration). In this article, we introduce different types of genomic data and data resources, and then review statistical methods of integrative genomics, with emphasis on the motivation and rationale of these methods. We conclude with some summary points and f...
Bayesian Methods for Statistical Analysis
Puza, Borek
2015-01-01
Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide variety of data. The book consists of 12 chapters, starting with basic concepts and covering numerous topics, including Bayesian estimation, decision theory, prediction, hypothesis testing, hierarchical models, Markov chain Monte Carlo methods, finite population inference, biased sampling and nonignorable nonresponse. The book contains many exercises, all with worked solutions, including complete c...
Nonparametric statistical methods using R
Kloke, John
2014-01-01
A Practical Guide to Implementing Nonparametric and Rank-Based ProceduresNonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses, including estimation and inference for models ranging from simple location models to general linear and nonlinear models for uncorrelated and correlated responses. The authors emphasize applications and statistical computation. They illustrate the methods with many real and simulated data examples using R, including the packages Rfit and npsm.The book first gives an overview of the R language and basic statistical c
Statistical Methods in Psychology Journals.
Willkinson, Leland
1999-01-01
Proposes guidelines for revising the American Psychological Association (APA) publication manual or other APA materials to clarify the application of statistics in research reports. The guidelines are intended to induce authors and editors to recognize the thoughtless application of statistical methods. Contains 54 references. (SLD)
Statistical Methods in Psychology Journals.
Willkinson, Leland
1999-01-01
Proposes guidelines for revising the American Psychological Association (APA) publication manual or other APA materials to clarify the application of statistics in research reports. The guidelines are intended to induce authors and editors to recognize the thoughtless application of statistical methods. Contains 54 references. (SLD)
Yang, Hai; Wei, Qiang; Zhong, Xue; Yang, Hushan; Li, Bingshan
2017-02-15
Comprehensive catalogue of genes that drive tumor initiation and progression in cancer is key to advancing diagnostics, therapeutics and treatment. Given the complexity of cancer, the catalogue is far from complete yet. Increasing evidence shows that driver genes exhibit consistent aberration patterns across multiple-omics in tumors. In this study, we aim to leverage complementary information encoded in each of the omics data to identify novel driver genes through an integrative framework. Specifically, we integrated mutations, gene expression, DNA copy numbers, DNA methylation and protein abundance, all available in The Cancer Genome Atlas (TCGA) and developed iDriver, a non-parametric Bayesian framework based on multivariate statistical modeling to identify driver genes in an unsupervised fashion. iDriver captures the inherent clusters of gene aberrations and constructs the background distribution that is used to assess and calibrate the confidence of driver genes identified through multi-dimensional genomic data. We applied the method to 4 cancer types in TCGA and identified candidate driver genes that are highly enriched with known drivers. (e.g.: P < 3.40 × 10 -36 for breast cancer). We are particularly interested in novel genes and observed multiple lines of supporting evidence. Using systematic evaluation from multiple independent aspects, we identified 45 candidate driver genes that were not previously known across these 4 cancer types. The finding has important implications that integrating additional genomic data with multivariate statistics can help identify cancer drivers and guide the next stage of cancer genomics research. The C ++ source code is freely available at https://medschool.vanderbilt.edu/cgg/ . hai.yang@vanderbilt.edu or bingshan.li@Vanderbilt.Edu. Supplementary data are available at Bioinformatics online.
SOLVING PROBLEMS OF STATISTICS WITH THE METHODS OF INFORMATION THEORY
Directory of Open Access Journals (Sweden)
Lutsenko Y. V.
2015-02-01
Full Text Available The article presents a theoretical substantiation, methods of numerical calculations and software implementation of the decision of problems of statistics, in particular the study of statistical distributions, methods of information theory. On the basis of empirical data by calculation we have determined the number of observations used for the analysis of statistical distributions. The proposed method of calculating the amount of information is not based on assumptions about the independence of observations and the normal distribution, i.e., is non-parametric and ensures the correct modeling of nonlinear systems, and also allows comparable to process heterogeneous (measured in scales of different types data numeric and non-numeric nature that are measured in different units. Thus, ASC-analysis and "Eidos" system is a modern innovation (ready for implementation technology solving problems of statistical methods of information theory. This article can be used as a description of the laboratory work in the disciplines of: intelligent systems; knowledge engineering and intelligent systems; intelligent technologies and knowledge representation; knowledge representation in intelligent systems; foundations of intelligent systems; introduction to neuromaturation and methods neural networks; fundamentals of artificial intelligence; intelligent technologies in science and education; knowledge management; automated system-cognitive analysis and "Eidos" intelligent system which the author is developing currently, but also in other disciplines associated with the transformation of data into information, and its transformation into knowledge and application of this knowledge to solve problems of identification, forecasting, decision making and research of the simulated subject area (which is virtually all subjects in all fields of science
Rapid Statistical Methods: Part 1.
Lyon, A. J.
1980-01-01
Discusses some rapid statistical methods which are intended for use by physics teachers. Part one of this article gives some of the simplest and most commonly useful rapid methods. Part two gives references to the relevant theory together with some alternative and additional methods. (HM)
Statistical methods in language processing.
Abney, Steven
2011-05-01
The term statistical methods here refers to a methodology that has been dominant in computational linguistics since about 1990. It is characterized by the use of stochastic models, substantial data sets, machine learning, and rigorous experimental evaluation. The shift to statistical methods in computational linguistics parallels a movement in artificial intelligence more broadly. Statistical methods have so thoroughly permeated computational linguistics that almost all work in the field draws on them in some way. There has, however, been little penetration of the methods into general linguistics. The methods themselves are largely borrowed from machine learning and information theory. We limit attention to that which has direct applicability to language processing, though the methods are quite general and have many nonlinguistic applications. Not every use of statistics in language processing falls under statistical methods as we use the term. Standard hypothesis testing and experimental design, for example, are not covered in this article. WIREs Cogni Sci 2011 2 315-322 DOI: 10.1002/wcs.111 For further resources related to this article, please visit the WIREs website.
Statistical methods for physical science
Stanford, John L
1994-01-01
This volume of Methods of Experimental Physics provides an extensive introduction to probability and statistics in many areas of the physical sciences, with an emphasis on the emerging area of spatial statistics. The scope of topics covered is wide-ranging-the text discusses a variety of the most commonly used classical methods and addresses newer methods that are applicable or potentially important. The chapter authors motivate readers with their insightful discussions, augmenting their material withKey Features* Examines basic probability, including coverage of standard distributions, time s
Statistical Methods for Evolutionary Trees
Edwards, A. W. F.
2009-01-01
In 1963 and 1964, L. L. Cavalli-Sforza and A. W. F. Edwards introduced novel methods for computing evolutionary trees from genetical data, initially for human populations from blood-group gene frequencies. The most important development was their introduction of statistical methods of estimation applied to stochastic models of evolution.
Statistical methods for evolutionary trees.
Edwards, A W F
2009-09-01
In 1963 and 1964, L. L. Cavalli-Sforza and A. W. F. Edwards introduced novel methods for computing evolutionary trees from genetical data, initially for human populations from blood-group gene frequencies. The most important development was their introduction of statistical methods of estimation applied to stochastic models of evolution.
Parametric and Non-Parametric Vibration-Based Structural Identification Under Earthquake Excitation
Pentaris, Fragkiskos P.; Fouskitakis, George N.
2014-05-01
The problem of modal identification in civil structures is of crucial importance, and thus has been receiving increasing attention in recent years. Vibration-based methods are quite promising as they are capable of identifying the structure's global characteristics, they are relatively easy to implement and they tend to be time effective and less expensive than most alternatives [1]. This paper focuses on the off-line structural/modal identification of civil (concrete) structures subjected to low-level earthquake excitations, under which, they remain within their linear operating regime. Earthquakes and their details are recorded and provided by the seismological network of Crete [2], which 'monitors' the broad region of south Hellenic arc, an active seismic region which functions as a natural laboratory for earthquake engineering of this kind. A sufficient number of seismic events are analyzed in order to reveal the modal characteristics of the structures under study, that consist of the two concrete buildings of the School of Applied Sciences, Technological Education Institute of Crete, located in Chania, Crete, Hellas. Both buildings are equipped with high-sensitivity and accuracy seismographs - providing acceleration measurements - established at the basement (structure's foundation) presently considered as the ground's acceleration (excitation) and at all levels (ground floor, 1st floor, 2nd floor and terrace). Further details regarding the instrumentation setup and data acquisition may be found in [3]. The present study invokes stochastic, both non-parametric (frequency-based) and parametric methods for structural/modal identification (natural frequencies and/or damping ratios). Non-parametric methods include Welch-based spectrum and Frequency response Function (FrF) estimation, while parametric methods, include AutoRegressive (AR), AutoRegressive with eXogeneous input (ARX) and Autoregressive Moving-Average with eXogeneous input (ARMAX) models[4, 5
Two new non-parametric tests to the distance duality relation with galaxy clusters
Costa, S S; Holanda, R F L
2015-01-01
The cosmic distance duality relation is a milestone of cosmology involving the luminosity and angular diameter distances. Any departure of the relation points to new physics or systematic errors in the observations, therefore tests of the relation are extremely important to build a consistent cosmological framework. Here, two new tests are proposed based on galaxy clusters observations (angular diameter distance and gas mass fraction) and $H(z)$ measurements. By applying Gaussian Processes, a non-parametric method, we are able to derive constraints on departures of the relation where no evidence of deviation is found in both methods, reinforcing the cosmological and astrophysical hypotheses adopted so far.
Beyond Statistical Methods – Compendium of Statistical Methods for Researchers
Directory of Open Access Journals (Sweden)
Ondřej Vozár
2014-12-01
Full Text Available Book Review: HENDL, J. Přehled statistických metod: Analýza a metaanalýza dat (Overview of Statistical Methods: Data Analysis and Metaanalysis. 4th extended edition. Prague: Portál, 2012. ISBN 978-80-262-0200-4.
Robust statistical methods with R
Jureckova, Jana
2005-01-01
Robust statistical methods were developed to supplement the classical procedures when the data violate classical assumptions. They are ideally suited to applied research across a broad spectrum of study, yet most books on the subject are narrowly focused, overly theoretical, or simply outdated. Robust Statistical Methods with R provides a systematic treatment of robust procedures with an emphasis on practical application.The authors work from underlying mathematical tools to implementation, paying special attention to the computational aspects. They cover the whole range of robust methods, including differentiable statistical functions, distance of measures, influence functions, and asymptotic distributions, in a rigorous yet approachable manner. Highlighting hands-on problem solving, many examples and computational algorithms using the R software supplement the discussion. The book examines the characteristics of robustness, estimators of real parameter, large sample properties, and goodness-of-fit tests. It...
Statistical methods for bioimpedance analysis
Directory of Open Access Journals (Sweden)
Christian Tronstad
2014-04-01
Full Text Available This paper gives a basic overview of relevant statistical methods for the analysis of bioimpedance measurements, with an aim to answer questions such as: How do I begin with planning an experiment? How many measurements do I need to take? How do I deal with large amounts of frequency sweep data? Which statistical test should I use, and how do I validate my results? Beginning with the hypothesis and the research design, the methodological framework for making inferences based on measurements and statistical analysis is explained. This is followed by a brief discussion on correlated measurements and data reduction before an overview is given of statistical methods for comparison of groups, factor analysis, association, regression and prediction, explained in the context of bioimpedance research. The last chapter is dedicated to the validation of a new method by different measures of performance. A flowchart is presented for selection of statistical method, and a table is given for an overview of the most important terms of performance when evaluating new measurement technology.
Statistical Methods for Fuzzy Data
Viertl, Reinhard
2011-01-01
Statistical data are not always precise numbers, or vectors, or categories. Real data are frequently what is called fuzzy. Examples where this fuzziness is obvious are quality of life data, environmental, biological, medical, sociological and economics data. Also the results of measurements can be best described by using fuzzy numbers and fuzzy vectors respectively. Statistical analysis methods have to be adapted for the analysis of fuzzy data. In this book, the foundations of the description of fuzzy data are explained, including methods on how to obtain the characterizing function of fuzzy m
Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data.
Tan, Qihua; Thomassen, Mads; Burton, Mark; Mose, Kristian Fredløv; Andersen, Klaus Ejner; Hjelmborg, Jacob; Kruse, Torben
2017-06-06
Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.
Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data
DEFF Research Database (Denmark)
Tan, Qihua; Thomassen, Mads; Burton, Mark
2017-01-01
Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering...... the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray...... time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health....
Non-parametric trend analysis of water quality data of rivers in Kansas
Yu, Y.-S.; Zou, S.; Whittemore, D.
1993-01-01
Surface water quality data for 15 sampling stations in the Arkansas, Verdigris, Neosho, and Walnut river basins inside the state of Kansas were analyzed to detect trends (or lack of trends) in 17 major constituents by using four different non-parametric methods. The results show that concentrations of specific conductance, total dissolved solids, calcium, total hardness, sodium, potassium, alkalinity, sulfate, chloride, total phosphorus, ammonia plus organic nitrogen, and suspended sediment generally have downward trends. Some of the downward trends are related to increases in discharge, while others could be caused by decreases in pollution sources. Homogeneity tests show that both station-wide trends and basinwide trends are non-homogeneous. ?? 1993.
Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data
DEFF Research Database (Denmark)
Tan, Qihua; Thomassen, Mads; Burton, Mark
2017-01-01
Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering...... the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray...
Statistical methods in spatial genetics
DEFF Research Database (Denmark)
Guillot, Gilles; Leblois, Raphael; Coulon, Aurelie
2009-01-01
The joint analysis of spatial and genetic data is rapidly becoming the norm in population genetics. More and more studies explicitly describe and quantify the spatial organization of genetic variation and try to relate it to underlying ecological processes. As it has become increasingly difficult...... to keep abreast with the latest methodological developments, we review the statistical toolbox available to analyse population genetic data in a spatially explicit framework. We mostly focus on statistical concepts but also discuss practical aspects of the analytical methods, highlighting not only...
[Evaluation of using statistical methods in selected national medical journals].
Sych, Z
1996-01-01
most important methods of mathematical statistics such as parametric tests of significance, analysis of variance (in single and dual classifications). non-parametric tests of significance, correlation and regression. The works, in which use was made of either multiple correlation or multiple regression or else more complex methods of studying the relationship for two or more numbers of variables, were incorporated into the works whose statistical methods were constituted by correlation and regression as well as other methods, e.g. statistical methods being used in epidemiology (coefficients of incidence and morbidity, standardization of coefficients, survival tables) factor analysis conducted by Jacobi-Hotellng's method, taxonomic methods and others. On the basis of the performed studies it has been established that the frequency of employing statistical methods in the six selected national, medical journals in the years 1988-1992 was 61.1-66.0% of the analyzed works (Tab. 3), and they generally were almost similar to the frequency provided in English language medical journals. On a whole, no significant differences were disclosed in the frequency of applied statistical methods (Tab. 4) as well as in frequency of random tests (Tab. 3) in the analyzed works, appearing in the medical journals in respective years 1988-1992. The most frequently used statistical methods in analyzed works for 1988-1992 were the measures of position 44.2-55.6% and measures of dispersion 32.5-38.5% as well as parametric tests of significance 26.3-33.1% of the works analyzed (Tab. 4). For the purpose of increasing the frequency and reliability of the used statistical methods, the didactics should be widened in the field of biostatistics at medical studies and postgraduation training designed for physicians and scientific-didactic workers.
A non-parametric approach to estimate the total deviation index for non-normal data.
Perez-Jaume, Sara; Carrasco, Josep L
2015-11-10
Concordance indices are used to assess the degree of agreement between different methods that measure the same characteristic. In this context, the total deviation index (TDI) is an unscaled concordance measure that quantifies to which extent the readings from the same subject obtained by different methods may differ with a certain probability. Common approaches to estimate the TDI assume data are normally distributed and linearity between response and effects (subjects, methods and random error). Here, we introduce a new non-parametric methodology for estimation and inference of the TDI that can deal with any kind of quantitative data. The present study introduces this non-parametric approach and compares it with the already established methods in two real case examples that represent situations of non-normal data (more specifically, skewed data and count data). The performance of the already established methodologies and our approach in these contexts is assessed by means of a simulation study. Copyright © 2015 John Wiley & Sons, Ltd.
Non-parametric iterative model constraint graph min-cut for automatic kidney segmentation.
Freiman, M; Kronman, A; Esses, S J; Joskowicz, L; Sosna, J
2010-01-01
We present a new non-parametric model constraint graph min-cut algorithm for automatic kidney segmentation in CT images. The segmentation is formulated as a maximum a-posteriori estimation of a model-driven Markov random field. A non-parametric hybrid shape and intensity model is treated as a latent variable in the energy functional. The latent model and labeling map that minimize the energy functional are then simultaneously computed with an expectation maximization approach. The main advantages of our method are that it does not assume a fixed parametric prior model, which is subjective to inter-patient variability and registration errors, and that it combines both the model and the image information into a unified graph min-cut based segmentation framework. We evaluated our method on 20 kidneys from 10 CT datasets with and without contrast agent for which ground-truth segmentations were generated by averaging three manual segmentations. Our method yields an average volumetric overlap error of 10.95%, and average symmetric surface distance of 0.79 mm. These results indicate that our method is accurate and robust for kidney segmentation.
Application of the LSQR algorithm in non-parametric estimation of aerosol size distribution
He, Zhenzong; Qi, Hong; Lew, Zhongyuan; Ruan, Liming; Tan, Heping; Luo, Kun
2016-05-01
Based on the Least Squares QR decomposition (LSQR) algorithm, the aerosol size distribution (ASD) is retrieved in non-parametric approach. The direct problem is solved by the Anomalous Diffraction Approximation (ADA) and the Lambert-Beer Law. An optimal wavelength selection method is developed to improve the retrieval accuracy of the ASD. The proposed optimal wavelength set is selected by the method which can make the measurement signals sensitive to wavelength and decrease the degree of the ill-condition of coefficient matrix of linear systems effectively to enhance the anti-interference ability of retrieval results. Two common kinds of monomodal and bimodal ASDs, log-normal (L-N) and Gamma distributions, are estimated, respectively. Numerical tests show that the LSQR algorithm can be successfully applied to retrieve the ASD with high stability in the presence of random noise and low susceptibility to the shape of distributions. Finally, the experimental measurement ASD over Harbin in China is recovered reasonably. All the results confirm that the LSQR algorithm combined with the optimal wavelength selection method is an effective and reliable technique in non-parametric estimation of ASD.
Multi-Directional Non-Parametric Analysis of Agricultural Efficiency
DEFF Research Database (Denmark)
Balezentis, Tomas
This thesis seeks to develop methodologies for assessment of agricultural efficiency and employ them to Lithuanian family farms. In particular, we focus on three particular objectives throughout the research: (i) to perform a fully non-parametric analysis of efficiency effects, (ii) to extend...... relative to labour, intermediate consumption and land (in some cases land was not treated as a discretionary input). These findings call for further research on relationships among financial structure, investment decisions, and efficiency in Lithuanian family farms. Application of different techniques...... of stochasticity associated with Lithuanian family farm performance. The former technique showed that the farms differed in terms of the mean values and variance of the efficiency scores over time with some clear patterns prevailing throughout the whole research period. The fuzzy Free Disposal Hull showed...
Validation of two (parametric vs non-parametric) daily weather generators
Dubrovsky, M.; Skalak, P.
2015-12-01
As the climate models (GCMs and RCMs) fail to satisfactorily reproduce the real-world surface weather regime, various statistical methods are applied to downscale GCM/RCM outputs into site-specific weather series. The stochastic weather generators are among the most favourite downscaling methods capable to produce realistic (observed-like) meteorological inputs for agrological, hydrological and other impact models used in assessing sensitivity of various ecosystems to climate change/variability. To name their advantages, the generators may (i) produce arbitrarily long multi-variate synthetic weather series representing both present and changed climates (in the latter case, the generators are commonly modified by GCM/RCM-based climate change scenarios), (ii) be run in various time steps and for multiple weather variables (the generators reproduce the correlations among variables), (iii) be interpolated (and run also for sites where no weather data are available to calibrate the generator). This contribution will compare two stochastic daily weather generators in terms of their ability to reproduce various features of the daily weather series. M&Rfi is a parametric generator: Markov chain model is used to model precipitation occurrence, precipitation amount is modelled by the Gamma distribution, and the 1st order autoregressive model is used to generate non-precipitation surface weather variables. The non-parametric GoMeZ generator is based on the nearest neighbours resampling technique making no assumption on the distribution of the variables being generated. Various settings of both weather generators will be assumed in the present validation tests. The generators will be validated in terms of (a) extreme temperature and precipitation characteristics (annual and 30-years extremes and maxima of duration of hot/cold/dry/wet spells); (b) selected validation statistics developed within the frame of VALUE project. The tests will be based on observational weather series
Material analysis on engineering statistics
Energy Technology Data Exchange (ETDEWEB)
Lee, Seung Hun
2008-03-15
This book is about material analysis on engineering statistics using mini tab, which includes technical statistics and seven tools of QC, probability distribution, presumption and checking, regression analysis, tim series analysis, control chart, process capacity analysis, measurement system analysis, sampling check, experiment planning, response surface analysis, compound experiment, Taguchi method, and non parametric statistics. It is good for university and company to use because it deals with theory first and analysis using mini tab on 6 sigma BB and MBB.
Non-parametric transformation for data correlation and integration: From theory to practice
Energy Technology Data Exchange (ETDEWEB)
Datta-Gupta, A.; Xue, Guoping; Lee, Sang Heon [Texas A& M Univ., College Station, TX (United States)
1997-08-01
The purpose of this paper is two-fold. First, we introduce the use of non-parametric transformations for correlating petrophysical data during reservoir characterization. Such transformations are completely data driven and do not require a priori functional relationship between response and predictor variables which is the case with traditional multiple regression. The transformations are very general, computationally efficient and can easily handle mixed data types for example, continuous variables such as porosity, permeability and categorical variables such as rock type, lithofacies. The power of the non-parametric transformation techniques for data correlation has been illustrated through synthetic and field examples. Second, we utilize these transformations to propose a two-stage approach for data integration during heterogeneity characterization. The principal advantages of our approach over traditional cokriging or cosimulation methods are: (1) it does not require a linear relationship between primary and secondary data, (2) it exploits the secondary information to its fullest potential by maximizing the correlation between the primary and secondary data, (3) it can be easily applied to cases where several types of secondary or soft data are involved, and (4) it significantly reduces variance function calculations and thus, greatly facilitates non-Gaussian cosimulation. We demonstrate the data integration procedure using synthetic and field examples. The field example involves estimation of pore-footage distribution using well data and multiple seismic attributes.
A non-parametric Bayesian approach for clustering and tracking non-stationarities of neural spikes.
Shalchyan, Vahid; Farina, Dario
2014-02-15
Neural spikes from multiple neurons recorded in a multi-unit signal are usually separated by clustering. Drifts in the position of the recording electrode relative to the neurons over time cause gradual changes in the position and shapes of the clusters, challenging the clustering task. By dividing the data into short time intervals, Bayesian tracking of the clusters based on Gaussian cluster model has been previously proposed. However, the Gaussian cluster model is often not verified for neural spikes. We present a Bayesian clustering approach that makes no assumptions on the distribution of the clusters and use kernel-based density estimation of the clusters in every time interval as a prior for Bayesian classification of the data in the subsequent time interval. The proposed method was tested and compared to Gaussian model-based approach for cluster tracking by using both simulated and experimental datasets. The results showed that the proposed non-parametric kernel-based density estimation of the clusters outperformed the sequential Gaussian model fitting in both simulated and experimental data tests. Using non-parametric kernel density-based clustering that makes no assumptions on the distribution of the clusters enhances the ability of tracking cluster non-stationarity over time with respect to the Gaussian cluster modeling approach. Copyright © 2013 Elsevier B.V. All rights reserved.
Order statistics & inference estimation methods
Balakrishnan, N
1991-01-01
The literature on order statistics and inferenc eis quite extensive and covers a large number of fields ,but most of it is dispersed throughout numerous publications. This volume is the consolidtion of the most important results and places an emphasis on estimation. Both theoretical and computational procedures are presented to meet the needs of researchers, professionals, and students. The methods of estimation discussed are well-illustrated with numerous practical examples from both the physical and life sciences, including sociology,psychology,a nd electrical and chemical engineering. A co
Assessing T cell clonal size distribution: a non-parametric approach.
Bolkhovskaya, Olesya V; Zorin, Daniil Yu; Ivanchenko, Mikhail V
2014-01-01
Clonal structure of the human peripheral T-cell repertoire is shaped by a number of homeostatic mechanisms, including antigen presentation, cytokine and cell regulation. Its accurate tuning leads to a remarkable ability to combat pathogens in all their variety, while systemic failures may lead to severe consequences like autoimmune diseases. Here we develop and make use of a non-parametric statistical approach to assess T cell clonal size distributions from recent next generation sequencing data. For 41 healthy individuals and a patient with ankylosing spondylitis, who undergone treatment, we invariably find power law scaling over several decades and for the first time calculate quantitatively meaningful values of decay exponent. It has proved to be much the same among healthy donors, significantly different for an autoimmune patient before the therapy, and converging towards a typical value afterwards. We discuss implications of the findings for theoretical understanding and mathematical modeling of adaptive immunity.
Assessing T cell clonal size distribution: a non-parametric approach.
Directory of Open Access Journals (Sweden)
Olesya V Bolkhovskaya
Full Text Available Clonal structure of the human peripheral T-cell repertoire is shaped by a number of homeostatic mechanisms, including antigen presentation, cytokine and cell regulation. Its accurate tuning leads to a remarkable ability to combat pathogens in all their variety, while systemic failures may lead to severe consequences like autoimmune diseases. Here we develop and make use of a non-parametric statistical approach to assess T cell clonal size distributions from recent next generation sequencing data. For 41 healthy individuals and a patient with ankylosing spondylitis, who undergone treatment, we invariably find power law scaling over several decades and for the first time calculate quantitatively meaningful values of decay exponent. It has proved to be much the same among healthy donors, significantly different for an autoimmune patient before the therapy, and converging towards a typical value afterwards. We discuss implications of the findings for theoretical understanding and mathematical modeling of adaptive immunity.
DEFF Research Database (Denmark)
Henningsen, Geraldine; Henningsen, Arne; Henning, Christian H. C. A.
All business transactions as well as achieving innovations take up resources, subsumed under the concept of transaction costs (TAC). One of the major factors in TAC theory is information. Information networks can catalyse the interpersonal information exchange and hence, increase the access...... to nonpublic information. Our analysis shows that information networks have an impact on the level of TAC. Many resources that are sacrificed for TAC are inputs that also enter the technical production process. As most production data do not separate between these two usages of inputs, high transaction costs...... are unveiled by reduced productivity. A cross-validated local linear non-parametric regression shows that good information networks increase the productivity of farms. A bootstrapping procedure confirms that this result is statistically significant....
Goerg, Georg M
2012-01-01
We present a new, non-parametric forecasting method for data where continuous values are observed discretely in space and time. Our method, "light-cone reconstruction of states" (LICORS), uses physical principles to identify predictive states which are local properties of the system, both in space and time. LICORS discovers the number of predictive states and their predictive distributions automatically, and consistently, under mild assumptions on the data source. We provide an algorithm to implement our method, along with a cross-validation scheme to pick control settings. Simulations show that CV-tuned LICORS outperforms standard methods in forecasting challenging spatio-temporal dynamics. Our work provides applied researchers with a new, highly automatic method to analyze and forecast spatio-temporal data.
Bayes linear statistics, theory & methods
Goldstein, Michael
2007-01-01
Bayesian methods combine information available from data with any prior information available from expert knowledge. The Bayes linear approach follows this path, offering a quantitative structure for expressing beliefs, and systematic methods for adjusting these beliefs, given observational data. The methodology differs from the full Bayesian methodology in that it establishes simpler approaches to belief specification and analysis based around expectation judgements. Bayes Linear Statistics presents an authoritative account of this approach, explaining the foundations, theory, methodology, and practicalities of this important field. The text provides a thorough coverage of Bayes linear analysis, from the development of the basic language to the collection of algebraic results needed for efficient implementation, with detailed practical examples. The book covers:The importance of partial prior specifications for complex problems where it is difficult to supply a meaningful full prior probability specification...
THE GROWTH POINTS OF STATISTICAL METHODS
Directory of Open Access Journals (Sweden)
Orlov A. I.
2014-11-01
Full Text Available On the basis of a new paradigm of applied mathematical statistics, data analysis and economic-mathematical methods are identified; we have also discussed five topical areas in which modern applied statistics is developing as well as the other statistical methods, i.e. five "growth points" – nonparametric statistics, robustness, computer-statistical methods, statistics of interval data, statistics of non-numeric data
Developing two non-parametric performance models for higher learning institutions
Kasim, Maznah Mat; Kashim, Rosmaini; Rahim, Rahela Abdul; Khan, Sahubar Ali Muhamed Nadhar
2016-08-01
Measuring the performance of higher learning Institutions (HLIs) is a must for these institutions to improve their excellence. This paper focuses on formation of two performance models: efficiency and effectiveness models by utilizing a non-parametric method, Data Envelopment Analysis (DEA). The proposed models are validated by measuring the performance of 16 public universities in Malaysia for year 2008. However, since data for one of the variables is unavailable, an estimate was used as a proxy to represent the real data. The results show that average efficiency and effectiveness scores were 0.817 and 0.900 respectively, while six universities were fully efficient and eight universities were fully effective. A total of six universities were both efficient and effective. It is suggested that the two proposed performance models would work as complementary methods to the existing performance appraisal method or as alternative methods in monitoring the performance of HLIs especially in Malaysia.
Wang, Ying; Wu, Fengchang; Giesy, John P; Feng, Chenglian; Liu, Yuedan; Qin, Ning; Zhao, Yujie
2015-09-01
Due to use of different parametric models for establishing species sensitivity distributions (SSDs), comparison of water quality criteria (WQC) for metals of the same group or period in the periodic table is uncertain and results can be biased. To address this inadequacy, a new probabilistic model, based on non-parametric kernel density estimation was developed and optimal bandwidths and testing methods are proposed. Zinc (Zn), cadmium (Cd), and mercury (Hg) of group IIB of the periodic table are widespread in aquatic environments, mostly at small concentrations, but can exert detrimental effects on aquatic life and human health. With these metals as target compounds, the non-parametric kernel density estimation method and several conventional parametric density estimation methods were used to derive acute WQC of metals for protection of aquatic species in China that were compared and contrasted with WQC for other jurisdictions. HC5 values for protection of different types of species were derived for three metals by use of non-parametric kernel density estimation. The newly developed probabilistic model was superior to conventional parametric density estimations for constructing SSDs and for deriving WQC for these metals. HC5 values for the three metals were inversely proportional to atomic number, which means that the heavier atoms were more potent toxicants. The proposed method provides a novel alternative approach for developing SSDs that could have wide application prospects in deriving WQC and use in assessment of risks to ecosystems.
Song, Dong; Wang, Zhuo; Marmarelis, Vasilis Z; Berger, Theodore W
2009-02-01
This paper presents a synergistic parametric and non-parametric modeling study of short-term plasticity (STP) in the Schaffer collateral to hippocampal CA1 pyramidal neuron (SC) synapse. Parametric models in the form of sets of differential and algebraic equations have been proposed on the basis of the current understanding of biological mechanisms active within the system. Non-parametric Poisson-Volterra models are obtained herein from broadband experimental input-output data. The non-parametric model is shown to provide better prediction of the experimental output than a parametric model with a single set of facilitation/depression (FD) process. The parametric model is then validated in terms of its input-output transformational properties using the non-parametric model since the latter constitutes a canonical and more complete representation of the synaptic nonlinear dynamics. Furthermore, discrepancies between the experimentally-derived non-parametric model and the equivalent non-parametric model of the parametric model suggest the presence of multiple FD processes in the SC synapses. Inclusion of an additional set of FD process in the parametric model makes it replicate better the characteristics of the experimentally-derived non-parametric model. This improved parametric model in turn provides the requisite biological interpretability that the non-parametric model lacks.
Non-parametric PSF estimation from celestial transit solar images using blind deconvolution
González, Adriana; Delouille, Véronique; Jacques, Laurent
2016-01-01
Context: Characterization of instrumental effects in astronomical imaging is important in order to extract accurate physical information from the observations. The measured image in a real optical instrument is usually represented by the convolution of an ideal image with a Point Spread Function (PSF). Additionally, the image acquisition process is also contaminated by other sources of noise (read-out, photon-counting). The problem of estimating both the PSF and a denoised image is called blind deconvolution and is ill-posed. Aims: We propose a blind deconvolution scheme that relies on image regularization. Contrarily to most methods presented in the literature, our method does not assume a parametric model of the PSF and can thus be applied to any telescope. Methods: Our scheme uses a wavelet analysis prior model on the image and weak assumptions on the PSF. We use observations from a celestial transit, where the occulting body can be assumed to be a black disk. These constraints allow us to retain meaningful solutions for the filter and the image, eliminating trivial, translated, and interchanged solutions. Under an additive Gaussian noise assumption, they also enforce noise canceling and avoid reconstruction artifacts by promoting the whiteness of the residual between the blurred observations and the cleaned data. Results: Our method is applied to synthetic and experimental data. The PSF is estimated for the SECCHI/EUVI instrument using the 2007 Lunar transit, and for SDO/AIA using the 2012 Venus transit. Results show that the proposed non-parametric blind deconvolution method is able to estimate the core of the PSF with a similar quality to parametric methods proposed in the literature. We also show that, if these parametric estimations are incorporated in the acquisition model, the resulting PSF outperforms both the parametric and non-parametric methods.
Non-parametric PSF estimation from celestial transit solar images using blind deconvolution
Directory of Open Access Journals (Sweden)
González Adriana
2016-01-01
Full Text Available Context: Characterization of instrumental effects in astronomical imaging is important in order to extract accurate physical information from the observations. The measured image in a real optical instrument is usually represented by the convolution of an ideal image with a Point Spread Function (PSF. Additionally, the image acquisition process is also contaminated by other sources of noise (read-out, photon-counting. The problem of estimating both the PSF and a denoised image is called blind deconvolution and is ill-posed. Aims: We propose a blind deconvolution scheme that relies on image regularization. Contrarily to most methods presented in the literature, our method does not assume a parametric model of the PSF and can thus be applied to any telescope. Methods: Our scheme uses a wavelet analysis prior model on the image and weak assumptions on the PSF. We use observations from a celestial transit, where the occulting body can be assumed to be a black disk. These constraints allow us to retain meaningful solutions for the filter and the image, eliminating trivial, translated, and interchanged solutions. Under an additive Gaussian noise assumption, they also enforce noise canceling and avoid reconstruction artifacts by promoting the whiteness of the residual between the blurred observations and the cleaned data. Results: Our method is applied to synthetic and experimental data. The PSF is estimated for the SECCHI/EUVI instrument using the 2007 Lunar transit, and for SDO/AIA using the 2012 Venus transit. Results show that the proposed non-parametric blind deconvolution method is able to estimate the core of the PSF with a similar quality to parametric methods proposed in the literature. We also show that, if these parametric estimations are incorporated in the acquisition model, the resulting PSF outperforms both the parametric and non-parametric methods.
Non-parametric Reconstruction of Cluster Mass Distribution from Strong Lensing Modelling Abell 370
Abdel-Salam, H M; Williams, L L R
1997-01-01
We describe a new non-parametric technique for reconstructing the mass distribution in galaxy clusters with strong lensing, i.e., from multiple images of background galaxies. The observed positions and redshifts of the images are considered as rigid constraints and through the lens (ray-trace) equation they provide us with linear constraint equations. These constraints confine the mass distribution to some allowed region, which is then found by linear programming. Within this allowed region we study in detail the mass distribution with minimum mass-to-light variation; also some others, such as the smoothest mass distribution. The method is applied to the extensively studied cluster Abell 370, which hosts a giant luminous arc and several other multiply imaged background galaxies. Our mass maps are constrained by the observed positions and redshifts (spectroscopic or model-inferred by previous authors) of the giant arc and multiple image systems. The reconstructed maps obtained for A370 reveal a detailed mass d...
Depth Transfer: Depth Extraction from Video Using Non-Parametric Sampling.
Karsch, Kevin; Liu, Ce; Kang, Sing Bing
2014-11-01
We describe a technique that automatically generates plausible depth maps from videos using non-parametric depth sampling. We demonstrate our technique in cases where past methods fail (non-translating cameras and dynamic scenes). Our technique is applicable to single images as well as videos. For videos, we use local motion cues to improve the inferred depth maps, while optical flow is used to ensure temporal depth consistency. For training and evaluation, we use a Kinect-based system to collect a large data set containing stereoscopic videos with known depths. We show that our depth estimation technique outperforms the state-of-the-art on benchmark databases. Our technique can be used to automatically convert a monoscopic video into stereo for 3D visualization, and we demonstrate this through a variety of visually pleasing results for indoor and outdoor scenes, including results from the feature film Charade.
DEFF Research Database (Denmark)
Carrao, Hugo; Sepulcre, Guadalupe; Horion, Stéphanie Marie Anne F;
2013-01-01
for the period between 1998 and 2010. The time-series analysis of vegetation greenness is performed during the growing season with a non-parametric method, namely the seasonal Relative Greenness (RG) of spatially accumulated fAPAR. The Global Land Cover map of 2000 and the GlobCover maps of 2005/2006 and 2009......This study evaluates the relationship between the frequency and duration of meteorological droughts and the subsequent temporal changes on the quantity of actively photosynthesizing biomass (greenness) estimated from satellite imagery on rainfed croplands in Latin America. An innovative non...... Full Data Reanalysis precipitation time-series product, which ranges from January 1901 to December 2010 and is interpolated at the spatial resolution of 1° (decimal degree, DD). Vegetation greenness composites are derived from 10-daily SPOT-VEGETATION images at the spatial resolution of 1/112° DD...
Energy Technology Data Exchange (ETDEWEB)
Kopsaftopoulos, Fotis P; Fassois, Spilios D, E-mail: fkopsaf@mech.upatras.gr, E-mail: fassois@mech.upatras.gr [Stochastic Mechanical Systems and Automation (SMSA) Laboratory Department of Mechanical and Aeronautical Engineering University of Patras, GR 265 00 Patras (Greece)
2011-07-19
A comparative assessment of several vibration based statistical time series methods for Structural Health Monitoring (SHM) is presented via their application to a scale aircraft skeleton laboratory structure. A brief overview of the methods, which are either scalar or vector type, non-parametric or parametric, and pertain to either the response-only or excitation-response cases, is provided. Damage diagnosis, including both the detection and identification subproblems, is tackled via scalar or vector vibration signals. The methods' effectiveness is assessed via repeated experiments under various damage scenarios, with each scenario corresponding to the loosening of one or more selected bolts. The results of the study confirm the 'global' damage detection capability and effectiveness of statistical time series methods for SHM.
非参数项目反应理论回顾与展望%The Retrospect and Prospect of Non-parametric Item Response Theory
Institute of Scientific and Technical Information of China (English)
陈婧; 康春花; 钟晓玲
2013-01-01
相比参数项目反应理论，非参数项目反应理论提供了更吻合实践情境的理论框架。目前非参数项目反应理论研究主要关注参数估计方法及其比较、数据-模型拟合验证等方面，其应用研究则集中于量表修订及个性数据和项目功能差异分析，而在认知诊断理论基础上发展起来的非参数认知诊断理论更是凸显其应用优势。未来研究应更多侧重于非参数项目反应理论的实践应用，对非参数认知诊断理论的研究也值得关注，以充分发挥非参数方法在实践领域的应用优势。% Compared to parametric item response theory, non-parametric item response theory provide a more appropriate theoretical framework of practice situations. Non-parametric item response theory research focuses on parameter estimation methods and its comparison, data- model fitting verify etc. currently.Its applied research concentrate on scale amendments, personalized data and differential item functioning analysis. Non-parametric cognitive diagnostic theory which based on the parametric cognitive diagnostic theory gives prominence to the advantages of its application.To give full play to the advantages of non-parametric methods in practice,future studies should emphasis on the application of non-parametric item response theory while cognitive diagnosis of the non-parametric study is also worth of attention.
Statistical methods in radiation physics
Turner, James E; Bogard, James S
2012-01-01
This statistics textbook, with particular emphasis on radiation protection and dosimetry, deals with statistical solutions to problems inherent in health physics measurements and decision making. The authors begin with a description of our current understanding of the statistical nature of physical processes at the atomic level, including radioactive decay and interactions of radiation with matter. Examples are taken from problems encountered in health physics, and the material is presented such that health physicists and most other nuclear professionals will more readily understand the application of statistical principles in the familiar context of the examples. Problems are presented at the end of each chapter, with solutions to selected problems provided online. In addition, numerous worked examples are included throughout the text.
Statistical inference via fiducial methods
Salomé, Diemer
1998-01-01
In this thesis the attention is restricted to inductive reasoning using a mathematical probability model. A statistical procedure prescribes, for every theoretically possible set of data, the inference about the unknown of interest. ... Zie: Summary
Toward improved statistical methods for analyzing Cotinine-Biomarker health association data
Directory of Open Access Journals (Sweden)
Clark John D
2011-10-01
Full Text Available Abstract Background Serum cotinine, a metabolite of nicotine, is frequently used in research as a biomarker of recent tobacco smoke exposure. Historically, secondhand smoke (SHS research uses suboptimal statistical methods due to censored serum cotinine values, meaning a measurement below the limit of detection (LOD. Methods We compared commonly used methods for analyzing censored serum cotinine data using parametric and non-parametric techniques employing data from the 1999-2004 National Health and Nutrition Examination Surveys (NHANES. To illustrate the differences in associations obtained by various analytic methods, we compared parameter estimates for the association between cotinine and the inflammatory marker homocysteine using complete case analysis, single and multiple imputation, "reverse" Kaplan-Meier, and logistic regression models. Results Parameter estimates and statistical significance varied according to the statistical method used with censored serum cotinine values. Single imputation of censored values with either 0, LOD or LOD/√2 yielded similar estimates and significance; multiple imputation method yielded smaller estimates than the other methods and without statistical significance. Multiple regression modelling using the "reverse" Kaplan-Meier method yielded statistically significant estimates that were larger than those from parametric methods. Conclusions Analyses of serum cotinine data with values below the LOD require special attention. "Reverse" Kaplan-Meier was the only method inherently able to deal with censored data with multiple LODs, and may be the most accurate since it avoids data manipulation needed for use with other commonly used statistical methods. Additional research is needed into the identification of optimal statistical methods for analysis of SHS biomarkers subject to a LOD.
Statistical methods in translational medicine.
Chow, Shein-Chung; Tse, Siu-Keung; Lin, Min
2008-12-01
This study focuses on strategies and statistical considerations for assessment of translation in language (e.g. translation of case report forms in multinational clinical trials), information (e.g. translation of basic discoveries to the clinic) and technology (e.g. translation of Chinese diagnostic techniques to well-established clinical study endpoints) in pharmaceutical/clinical research and development. However, most of our efforts will be directed to statistical considerations for translation in information. Translational medicine has been defined as bench-to-bedside research, where a basic laboratory discovery becomes applicable to the diagnosis, treatment or prevention of a specific disease, and is brought forth by either a physicianscientist who works at the interface between the research laboratory and patient care, or by a team of basic and clinical science investigators. Statistics plays an important role in translational medicine to ensure that the translational process is accurate and reliable with certain statistical assurance. Statistical inference for the applicability of an animal model to a human model is also discussed. Strategies for selection of clinical study endpoints (e.g. absolute changes, relative changes, or responder-defined, based on either absolute or relative change) are reviewed.
Statistical Methods in Translational Medicine
Directory of Open Access Journals (Sweden)
Shein-Chung Chow
2008-12-01
Full Text Available This study focuses on strategies and statistical considerations for assessment of translation in language (e.g. translation of case report forms in multinational clinical trials, information (e.g. translation of basic discoveries to the clinic and technology (e.g. translation of Chinese diagnostic techniques to well-established clinical study endpoints in pharmaceutical/clinical research and development. However, most of our efforts will be directed to statistical considerations for translation in information. Translational medicine has been defined as bench-to-bedside research, where a basic laboratory discovery becomes applicable to the diagnosis, treatment or prevention of a specific disease, and is brought forth by either a physician—scientist who works at the interface between the research laboratory and patient care, or by a team of basic and clinical science investigators. Statistics plays an important role in translational medicine to ensure that the translational process is accurate and reliable with certain statistical assurance. Statistical inference for the applicability of an animal model to a human model is also discussed. Strategies for selection of clinical study endpoints (e.g. absolute changes, relative changes, or responder-defined, based on either absolute or relative change are reviewed.
Register-based statistics statistical methods for administrative data
Wallgren, Anders
2014-01-01
This book provides a comprehensive and up to date treatment of theory and practical implementation in Register-based statistics. It begins by defining the area, before explaining how to structure such systems, as well as detailing alternative approaches. It explains how to create statistical registers, how to implement quality assurance, and the use of IT systems for register-based statistics. Further to this, clear details are given about the practicalities of implementing such statistical methods, such as protection of privacy and the coordination and coherence of such an undertaking. Thi
Permutation statistical methods an integrated approach
Berry, Kenneth J; Johnston, Janis E
2016-01-01
This research monograph provides a synthesis of a number of statistical tests and measures, which, at first consideration, appear disjoint and unrelated. Numerous comparisons of permutation and classical statistical methods are presented, and the two methods are compared via probability values and, where appropriate, measures of effect size. Permutation statistical methods, compared to classical statistical methods, do not rely on theoretical distributions, avoid the usual assumptions of normality and homogeneity of variance, and depend only on the data at hand. This text takes a unique approach to explaining statistics by integrating a large variety of statistical methods, and establishing the rigor of a topic that to many may seem to be a nascent field in statistics. This topic is new in that it took modern computing power to make permutation methods available to people working in the mainstream of research. This research monograph addresses a statistically-informed audience, and can also easily serve as a ...
Schutte, Willem D.; Swanepoel, Jan W. H.
2016-09-01
An automated tool to derive the off-pulse interval of a light curve originating from a pulsar is needed. First, we derive a powerful and accurate non-parametric sequential estimation technique to estimate the off-pulse interval of a pulsar light curve in an objective manner. This is in contrast to the subjective `eye-ball' (visual) technique, and complementary to the Bayesian Block method which is currently used in the literature. The second aim involves the development of a statistical package, necessary for the implementation of our new estimation technique. We develop a statistical procedure to estimate the off-pulse interval in the presence of noise. It is based on a sequential application of p-values obtained from goodness-of-fit tests for uniformity. The Kolmogorov-Smirnov, Cramér-von Mises, Anderson-Darling and Rayleigh test statistics are applied. The details of the newly developed statistical package SOPIE (Sequential Off-Pulse Interval Estimation) are discussed. The developed estimation procedure is applied to simulated and real pulsar data. Finally, the SOPIE estimated off-pulse intervals of two pulsars are compared to the estimates obtained with the Bayesian Block method and yield very satisfactory results. We provide the code to implement the SOPIE package, which is publicly available at http://CRAN.R-project.org/package=SOPIE (Schutte).
Li, Ming; Gardiner, Joseph C; Breslau, Naomi; Anthony, James C; Lu, Qing
2014-07-01
Cox-regression-based methods have been commonly used for the analyses of survival outcomes, such as age-at-disease-onset. These methods generally assume the hazard functions are proportional among various risk groups. However, such an assumption may not be valid in genetic association studies, especially when complex interactions are involved. In addition, genetic association studies commonly adopt case-control designs. Direct use of Cox regression to case-control data may yield biased estimators and incorrect statistical inference. We propose a non-parametric approach, the weighted Nelson-Aalen (WNA) approach, for detecting genetic variants that are associated with age-dependent outcomes. The proposed approach can be directly applied to prospective cohort studies, and can be easily extended for population-based case-control studies. Moreover, it does not rely on any assumptions of the disease inheritance models, and is able to capture high-order gene-gene interactions. Through simulations, we show the proposed approach outperforms Cox-regression-based methods in various scenarios. We also conduct an empirical study of progression of nicotine dependence by applying the WNA approach to three independent datasets from the Study of Addiction: Genetics and Environment. In the initial dataset, two SNPs, rs6570989 and rs2930357, located in genes GRIK2 and CSMD1, are found to be significantly associated with the progression of nicotine dependence (ND). The joint association is further replicated in two independent datasets. Further analysis suggests that these two genes may interact and be associated with the progression of ND. As demonstrated by the simulation studies and real data analysis, the proposed approach provides an efficient tool for detecting genetic interactions associated with age-at-onset outcomes.
Kernel bandwidth estimation for non-parametric density estimation: a comparative study
CSIR Research Space (South Africa)
Van der Walt, CM
2013-12-01
Full Text Available We investigate the performance of conventional bandwidth estimators for non-parametric kernel density estimation on a number of representative pattern-recognition tasks, to gain a better understanding of the behaviour of these estimators in high...
分布函数的非参数最小二乘估计%NON-PARAMETRIC LEAST SQUARE ESTIMATION OF DISTRIBUTION FUNCTION
Institute of Scientific and Technical Information of China (English)
柴根象; 花虹; 尚汉冀
2002-01-01
By using the non-parametric least square method, the strong consistent estimations of distribution function and failure function are established,where the distribution function F(x) after logist transformation is assumed to be approximated by a polynomial.The performance of simulation shows that the estimations are highly satisfactory.
Climate Prediction through Statistical Methods
Akgun, Bora; Tuter, Levent; Kurnaz, Mehmet Levent
2008-01-01
Climate change is a reality of today. Paleoclimatic proxies and climate predictions based on coupled atmosphere-ocean general circulation models provide us with temperature data. Using Detrended Fluctuation Analysis, we are investigating the statistical connection between the climate types of the present and these local temperatures. We are relating this issue to some well-known historic climate shifts. Our main result is that the temperature fluctuations with or without a temperature scale attached to them, can be used to classify climates in the absence of other indicators such as pan evaporation and precipitation.
The research of railway freight statistics system and statistical methods
Directory of Open Access Journals (Sweden)
Wu Hua-Wen
2013-01-01
Full Text Available EXT is a JavaScript framework for developing Web interfaces, this paper describes the Ext framework and its application in railway freight statistical and analyzing system and Statistical methods. the paper also analyzes the design, function, implementation and so on of the system in detail. As information technology and the requirements of railway transportation organization and operation continue to improve, railway freight statistical and analyzing system improves obviously in the index system, decision analysis and other aspects, better meeting the work requirements. It will play a more important role in the railway transport organization, management, passenger and freight marketing.
Statistical Methods for Unusual Count Data
DEFF Research Database (Denmark)
Guthrie, Katherine A.; Gammill, Hilary S.; Kamper-Jørgensen, Mads
2016-01-01
microchimerism data present challenges for statistical analysis, including a skewed distribution, excess zero values, and occasional large values. Methods for comparing microchimerism levels across groups while controlling for covariates are not well established. We compared statistical models for quantitative...
Energy Technology Data Exchange (ETDEWEB)
Ford, Eric B.; /Florida U.; Fabrycky, Daniel C.; /Lick Observ.; Steffen, Jason H.; /Fermilab; Carter, Joshua A.; /Harvard-Smithsonian Ctr. Astrophys.; Fressin, Francois; /Harvard-Smithsonian Ctr. Astrophys.; Holman, Matthew J.; /Harvard-Smithsonian Ctr. Astrophys.; Lissauer, Jack J.; /NASA, Ames; Moorhead, Althea V.; /Florida U.; Morehead, Robert C.; /Florida U.; Ragozzine, Darin; /Harvard-Smithsonian Ctr. Astrophys.; Rowe, Jason F.; /NASA, Ames /SETI Inst., Mtn. View /San Diego State U., Astron. Dept.
2012-01-01
We present a new method for confirming transiting planets based on the combination of transit timing variations (TTVs) and dynamical stability. Correlated TTVs provide evidence that the pair of bodies are in the same physical system. Orbital stability provides upper limits for the masses of the transiting companions that are in the planetary regime. This paper describes a non-parametric technique for quantifying the statistical significance of TTVs based on the correlation of two TTV data sets. We apply this method to an analysis of the transit timing variations of two stars with multiple transiting planet candidates identified by Kepler. We confirm four transiting planets in two multiple planet systems based on their TTVs and the constraints imposed by dynamical stability. An additional three candidates in these same systems are not confirmed as planets, but are likely to be validated as real planets once further observations and analyses are possible. If all were confirmed, these systems would be near 4:6:9 and 2:4:6:9 period commensurabilities. Our results demonstrate that TTVs provide a powerful tool for confirming transiting planets, including low-mass planets and planets around faint stars for which Doppler follow-up is not practical with existing facilities. Continued Kepler observations will dramatically improve the constraints on the planet masses and orbits and provide sensitivity for detecting additional non-transiting planets. If Kepler observations were extended to eight years, then a similar analysis could likely confirm systems with multiple closely spaced, small transiting planets in or near the habitable zone of solar-type stars.
Statistical methods in physical mapping
Energy Technology Data Exchange (ETDEWEB)
Nelson, David O. [Univ. of California, Berkeley, CA (United States)
1995-05-01
One of the great success stories of modern molecular genetics has been the ability of biologists to isolate and characterize the genes responsible for serious inherited diseases like fragile X syndrome, cystic fibrosis and myotonic muscular dystrophy. This dissertation concentrates on constructing high-resolution physical maps. It demonstrates how probabilistic modeling and statistical analysis can aid molecular geneticists in the tasks of planning, execution, and evaluation of physical maps of chromosomes and large chromosomal regions. The dissertation is divided into six chapters. Chapter 1 provides an introduction to the field of physical mapping, describing the role of physical mapping in gene isolation and ill past efforts at mapping chromosomal regions. The next two chapters review and extend known results on predicting progress in large mapping projects. Such predictions help project planners decide between various approaches and tactics for mapping large regions of the human genome. Chapter 2 shows how probability models have been used in the past to predict progress in mapping projects. Chapter 3 presents new results, based on stationary point process theory, for progress measures for mapping projects based on directed mapping strategies. Chapter 4 describes in detail the construction of all initial high-resolution physical map for human chromosome 19. This chapter introduces the probability and statistical models involved in map construction in the context of a large, ongoing physical mapping project. Chapter 5 concentrates on one such model, the trinomial model. This chapter contains new results on the large-sample behavior of this model, including distributional results, asymptotic moments, and detection error rates. In addition, it contains an optimality result concerning experimental procedures based on the trinomial model. The last chapter explores unsolved problems and describes future work.
Statistical concepts a second course
Lomax, Richard G
2012-01-01
Statistical Concepts consists of the last 9 chapters of An Introduction to Statistical Concepts, 3rd ed. Designed for the second course in statistics, it is one of the few texts that focuses just on intermediate statistics. The book highlights how statistics work and what they mean to better prepare students to analyze their own data and interpret SPSS and research results. As such it offers more coverage of non-parametric procedures used when standard assumptions are violated since these methods are more frequently encountered when working with real data. Determining appropriate sample sizes
Multivariate statistical methods a primer
Manly, Bryan FJ
2004-01-01
THE MATERIAL OF MULTIVARIATE ANALYSISExamples of Multivariate DataPreview of Multivariate MethodsThe Multivariate Normal DistributionComputer ProgramsGraphical MethodsChapter SummaryReferencesMATRIX ALGEBRAThe Need for Matrix AlgebraMatrices and VectorsOperations on MatricesMatrix InversionQuadratic FormsEigenvalues and EigenvectorsVectors of Means and Covariance MatricesFurther Reading Chapter SummaryReferencesDISPLAYING MULTIVARIATE DATAThe Problem of Displaying Many Variables in Two DimensionsPlotting index VariablesThe Draftsman's PlotThe Representation of Individual Data P:ointsProfiles o
Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne
2012-12-01
In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.
Non-Parametric Evolutionary Algorithm for Estimating Root Zone Soil Moisture
Mohanty, B.; Shin, Y.; Ines, A. M.
2013-12-01
Prediction of root zone soil moisture is critical for water resources management. In this study, we explored a non-parametric evolutionary algorithm for estimating root zone soil moisture from a time series of spatially-distributed rainfall across multiple weather locations under two different hydro-climatic regions. A new genetic algorithm-based hidden Markov model (HMMGA) was developed to estimate long-term root zone soil moisture dynamics at different soil depths. Also, we analyzed rainfall occurrence probabilities and dry/wet spell lengths reproduced by this approach. The HMMGA was used to estimate the optimal state sequences (weather states) based on the precipitation history. Historical root zone soil moisture statistics were then determined based on the weather state conditions. To test the new approach, we selected two different soil moisture fields, Oklahoma (130 km x 130 km) and Illinois (300 km x 500 km), during 1995 to 2009 and 1994 to 2010, respectively. We found that the newly developed framework performed well in predicting root zone soil moisture dynamics at both the spatial scales. Also, the reproduced rainfall occurrence probabilities and dry/wet spell lengths matched well with the observations at the spatio-temporal scales. Since the proposed algorithm requires only precipitation and historical soil moisture data from existing, established weather stations, it can serve an attractive alternative for predicting root zone soil moisture in the future using climate change scenarios and root zone soil moisture history.
A Non-parametric Approach to Constrain the Transfer Function in Reverberation Mapping
Li, Yan-Rong; Wang, Jian-Min; Bai, Jin-Ming
2016-11-01
Broad emission lines of active galactic nuclei stem from a spatially extended region (broad-line region, BLR) that is composed of discrete clouds and photoionized by the central ionizing continuum. The temporal behaviors of these emission lines are blurred echoes of continuum variations (i.e., reverberation mapping, RM) and directly reflect the structures and kinematic information of BLRs through the so-called transfer function (also known as the velocity-delay map). Based on the previous works of Rybicki and Press and Zu et al., we develop an extended, non-parametric approach to determine the transfer function for RM data, in which the transfer function is expressed as a sum of a family of relatively displaced Gaussian response functions. Therefore, arbitrary shapes of transfer functions associated with complicated BLR geometry can be seamlessly included, enabling us to relax the presumption of a specified transfer function frequently adopted in previous studies and to let it be determined by observation data. We formulate our approach in a previously well-established framework that incorporates the statistical modeling of continuum variations as a damped random walk process and takes into account long-term secular variations which are irrelevant to RM signals. The application to RM data shows the fidelity of our approach.
Equilibrium Statistics: Monte Carlo Methods
Kröger, Martin
Monte Carlo methods use random numbers, or ‘random’ sequences, to sample from a known shape of a distribution, or to extract distribution by other means. and, in the context of this book, to (i) generate representative equilibrated samples prior being subjected to external fields, or (ii) evaluate high-dimensional integrals. Recipes for both topics, and some more general methods, are summarized in this chapter. It is important to realize, that Monte Carlo should be as artificial as possible to be efficient and elegant. Advanced Monte Carlo ‘moves’, required to optimize the speed of algorithms for a particular problem at hand, are outside the scope of this brief introduction. One particular modern example is the wavelet-accelerated MC sampling of polymer chains [406].
Statistical methods for nuclear material management
Energy Technology Data Exchange (ETDEWEB)
Bowen W.M.; Bennett, C.A. (eds.)
1988-12-01
This book is intended as a reference manual of statistical methodology for nuclear material management practitioners. It describes statistical methods currently or potentially important in nuclear material management, explains the choice of methods for specific applications, and provides examples of practical applications to nuclear material management problems. Together with the accompanying training manual, which contains fully worked out problems keyed to each chapter, this book can also be used as a textbook for courses in statistical methods for nuclear material management. It should provide increased understanding and guidance to help improve the application of statistical methods to nuclear material management problems.
Statistical Methods for Material Characterization and Qualification
Energy Technology Data Exchange (ETDEWEB)
Kercher, A.K.
2005-04-01
This document describes a suite of statistical methods that can be used to infer lot parameters from the data obtained from inspection/testing of random samples taken from that lot. Some of these methods will be needed to perform the statistical acceptance tests required by the Advanced Gas Reactor Fuel Development and Qualification (AGR) Program. Special focus has been placed on proper interpretation of acceptance criteria and unambiguous methods of reporting the statistical results. In addition, modified statistical methods are described that can provide valuable measures of quality for different lots of material. This document has been written for use as a reference and a guide for performing these statistical calculations. Examples of each method are provided. Uncertainty analysis (e.g., measurement uncertainty due to instrumental bias) is not included in this document, but should be considered when reporting statistical results.
Statistical methods for material characterization and qualification
Energy Technology Data Exchange (ETDEWEB)
Hunn, John D [ORNL; Kercher, Andrew K [ORNL
2005-01-01
This document describes a suite of statistical methods that can be used to infer lot parameters from the data obtained from inspection/testing of random samples taken from that lot. Some of these methods will be needed to perform the statistical acceptance tests required by the Advanced Gas Reactor Fuel Development and Qualification (AGR) Program. Special focus has been placed on proper interpretation of acceptance criteria and unambiguous methods of reporting the statistical results. In addition, modified statistical methods are described that can provide valuable measures of quality for different lots of material. This document has been written for use as a reference and a guide for performing these statistical calculations. Examples of each method are provided. Uncertainty analysis (e.g., measurement uncertainty due to instrumental bias) is not included in this document, but should be considered when reporting statistical results.
Statistical methods for material characterization and qualification
Energy Technology Data Exchange (ETDEWEB)
Hunn, John D [ORNL; Kercher, Andrew K [ORNL
2005-01-01
This document describes a suite of statistical methods that can be used to infer lot parameters from the data obtained from inspection/testing of random samples taken from that lot. Some of these methods will be needed to perform the statistical acceptance tests required by the Advanced Gas Reactor Fuel Development and Qualification (AGR) Program. Special focus has been placed on proper interpretation of acceptance criteria and unambiguous methods of reporting the statistical results. In addition, modified statistical methods are described that can provide valuable measures of quality for different lots of material. This document has been written for use as a reference and a guide for performing these statistical calculations. Examples of each method are provided. Uncertainty analysis (e.g., measurement uncertainty due to instrumental bias) is not included in this document, but should be considered when reporting statistical results.
Statistical Methods for Material Characterization and Qualification
Energy Technology Data Exchange (ETDEWEB)
Kercher, A.K.
2005-04-01
This document describes a suite of statistical methods that can be used to infer lot parameters from the data obtained from inspection/testing of random samples taken from that lot. Some of these methods will be needed to perform the statistical acceptance tests required by the Advanced Gas Reactor Fuel Development and Qualification (AGR) Program. Special focus has been placed on proper interpretation of acceptance criteria and unambiguous methods of reporting the statistical results. In addition, modified statistical methods are described that can provide valuable measures of quality for different lots of material. This document has been written for use as a reference and a guide for performing these statistical calculations. Examples of each method are provided. Uncertainty analysis (e.g., measurement uncertainty due to instrumental bias) is not included in this document, but should be considered when reporting statistical results.
Energy Technology Data Exchange (ETDEWEB)
Gonzalez-Manteiga, W.; Prada-Sanchez, J.M.; Fiestras-Janeiro, M.G.; Garcia-Jurado, I. (Universidad de Santiago de Compostela, Santiago de Compostela (Spain). Dept. de Estadistica e Investigacion Operativa)
1990-11-01
A statistical study of the dependence between various critical fusion temperatures of a certain kind of coal and its chemical components is carried out. As well as using classical dependence techniques (multiple, stepwise and PLS regression, principal components, canonical correlation, etc.) together with the corresponding inference on the parameters of interest, non-parametric regression and bootstrap inference are also performed. 11 refs., 3 figs., 8 tabs.
Harlander, Niklas; Rosenkranz, Tobias; Hohmann, Volker
2012-08-01
Single channel noise reduction has been well investigated and seems to have reached its limits in terms of speech intelligibility improvement, however, the quality of such schemes can still be advanced. This study tests to what extent novel model-based processing schemes might improve performance in particular for non-stationary noise conditions. Two prototype model-based algorithms, a speech-model-based, and a auditory-model-based algorithm were compared to a state-of-the-art non-parametric minimum statistics algorithm. A speech intelligibility test, preference rating, and listening effort scaling were performed. Additionally, three objective quality measures for the signal, background, and overall distortions were applied. For a better comparison of all algorithms, particular attention was given to the usage of the similar Wiener-based gain rule. The perceptual investigation was performed with fourteen hearing-impaired subjects. The results revealed that the non-parametric algorithm and the auditory model-based algorithm did not affect speech intelligibility, whereas the speech-model-based algorithm slightly decreased intelligibility. In terms of subjective quality, both model-based algorithms perform better than the unprocessed condition and the reference in particular for highly non-stationary noise environments. Data support the hypothesis that model-based algorithms are promising for improving performance in non-stationary noise conditions.
THE DARK MATTER PROFILE OF THE MILKY WAY: A NON-PARAMETRIC RECONSTRUCTION
Energy Technology Data Exchange (ETDEWEB)
Pato, Miguel [The Oskar Klein Centre for Cosmoparticle Physics, Department of Physics, Stockholm University, AlbaNova, SE-106 91 Stockholm (Sweden); Iocco, Fabio [ICTP South American Institute for Fundamental Research, and Instituto de Física Teórica—Universidade Estadual Paulista (UNESP), Rua Dr. Bento Teobaldo Ferraz 271, 01140-070 São Paulo, SP (Brazil)
2015-04-10
We present the results of a new, non-parametric method to reconstruct the Galactic dark matter profile directly from observations. Using the latest kinematic data to track the total gravitational potential and the observed distribution of stars and gas to set the baryonic component, we infer the dark matter contribution to the circular velocity across the Galaxy. The radial derivative of this dynamical contribution is then estimated to extract the dark matter profile. The innovative feature of our approach is that it makes no assumption on the functional form or shape of the profile, thus allowing for a clean determination with no theoretical bias. We illustrate the power of the method by constraining the spherical dark matter profile between 2.5 and 25 kpc away from the Galactic center. The results show that the proposed method, free of widely used assumptions, can already be applied to pinpoint the dark matter distribution in the Milky Way with competitive accuracy, and paves the way for future developments.
Statistical Models and Methods for Lifetime Data
Lawless, Jerald F
2011-01-01
Praise for the First Edition"An indispensable addition to any serious collection on lifetime data analysis and . . . a valuable contribution to the statistical literature. Highly recommended . . ."-Choice"This is an important book, which will appeal to statisticians working on survival analysis problems."-Biometrics"A thorough, unified treatment of statistical models and methods used in the analysis of lifetime data . . . this is a highly competent and agreeable statistical textbook."-Statistics in MedicineThe statistical analysis of lifetime or response time data is a key tool in engineering,
Maydeu-Olivares, Albert
2005-04-01
Chernyshenko, Stark, Chan, Drasgow, and Williams (2001) investigated the fit of Samejima's logistic graded model and Levine's non-parametric MFS model to the scales of two personality questionnaires and found that the graded model did not fit well. We attribute the poor fit of the graded model to small amounts of multidimensionality present in their data. To verify this conjecture, we compare the fit of these models to the Social Problem Solving Inventory-Revised, whose scales were designed to be unidimensional. A calibration and a cross-validation sample of new observations were used. We also included the following parametric models in the comparison: Bock's nominal model, Masters' partial credit model, and Thissen and Steinberg's extension of the latter. All models were estimated using full information maximum likelihood. We also included in the comparison a normal ogive model version of Samejima's model estimated using limited information estimation. We found that for all scales Samejima's model outperformed all other parametric IRT models in both samples, regardless of the estimation method employed. The non-parametric model outperformed all parametric models in the calibration sample. However, the graded model outperformed MFS in the cross-validation sample in some of the scales. We advocate employing the graded model estimated using limited information methods in modeling Likert-type data, as these methods are more versatile than full information methods to capture the multidimensionality that is generally present in personality data.
A Non-Parametric Delphi Approach to Foster Innovation Policy Debate in Spain
Directory of Open Access Journals (Sweden)
Juan Carlos Salazar-Elena
2016-05-01
Full Text Available The aim of this paper is to identify some changes needed in Spain’s innovation policy to fill the gap between its innovation results and those of other European countries in lieu of sustainable leadership. To do this we apply the Delphi methodology to experts from academia, business, and government. To overcome the shortcomings of traditional descriptive methods, we develop an inferential analysis by following a non-parametric bootstrap method which enables us to identify important changes that should be implemented. Particularly interesting is the support found for improving the interconnections among the relevant agents of the innovation system (instead of focusing exclusively in the provision of knowledge and technological inputs through R and D activities, or the support found for “soft” policy instruments aimed at providing a homogeneous framework to assess the innovation capabilities of firms (e.g., for funding purposes. Attention to potential innovators among small and medium enterprises (SMEs and traditional industries is particularly encouraged by experts.
Multivariate statistical methods a first course
Marcoulides, George A
2014-01-01
Multivariate statistics refer to an assortment of statistical methods that have been developed to handle situations in which multiple variables or measures are involved. Any analysis of more than two variables or measures can loosely be considered a multivariate statistical analysis. An introductory text for students learning multivariate statistical methods for the first time, this book keeps mathematical details to a minimum while conveying the basic principles. One of the principal strategies used throughout the book--in addition to the presentation of actual data analyses--is poin
Vale, A
2007-01-01
We revisit the longstanding question of whether first brightest cluster galaxies are statistically drawn from the same distribution as other cluster galaxies or are "special", using the new non-parametric, empirically based model presented in Vale&Ostriker (2006) for associating galaxy luminosity with halo/subhalo masses. We introduce scatter in galaxy luminosity at fixed halo mass into this model, building a conditional luminosity function (CLF) by considering two possible models: a simple lognormal and a model based on the distribution of concentration in haloes of a given mass. We show that this model naturally allows an identification of halo/subhalo systems with groups and clusters of galaxies, giving rise to a clear central/satellite galaxy distinction. We then use these results to build up the dependence of brightest cluster galaxy (BCG) magnitudes on cluster luminosity, focusing on two statistical indicators, the dispersion in BCG magnitude and the magnitude difference between first and second bri...
SOME STATISTICAL SOFTWARE APPLICATIONS FOR TAGUCHI METHODS
Directory of Open Access Journals (Sweden)
Adrian Stere PARIS
2016-05-01
Full Text Available The paper details the variety of Taguchi methods, as important contribution to the quality improvement. The extended use of these methods imposes more and more complex calculi for the practical application and optimization. It should be necessary to benefit by the new software developments, assisted by the advanced statistical methods. The paper presents a few particular applications of some statistical software for the Taguchi methods as a quality enhancement insisting on the quality loss functions, the design of experiments and the new developments of statistical process control.
Advanced statistical methods in data science
Chen, Jiahua; Lu, Xuewen; Yi, Grace; Yu, Hao
2016-01-01
This book gathers invited presentations from the 2nd Symposium of the ICSA- CANADA Chapter held at the University of Calgary from August 4-6, 2015. The aim of this Symposium was to promote advanced statistical methods in big-data sciences and to allow researchers to exchange ideas on statistics and data science and to embraces the challenges and opportunities of statistics and data science in the modern world. It addresses diverse themes in advanced statistical analysis in big-data sciences, including methods for administrative data analysis, survival data analysis, missing data analysis, high-dimensional and genetic data analysis, longitudinal and functional data analysis, the design and analysis of studies with response-dependent and multi-phase designs, time series and robust statistics, statistical inference based on likelihood, empirical likelihood and estimating functions. The editorial group selected 14 high-quality presentations from this successful symposium and invited the presenters to prepare a fu...
DEFF Research Database (Denmark)
Ramirez, José Rangel; Sørensen, John Dalsgaard
2011-01-01
This work illustrates the updating and incorporation of information in the assessment of fatigue reliability for offshore wind turbine. The new information, coming from external and condition monitoring can be used to direct updating of the stochastic variables through a non-parametric Bayesian...... updating approach and be integrated in the reliability analysis by a third-order polynomial chaos expansion approximation. Although Classical Bayesian updating approaches are often used because of its parametric formulation, non-parametric approaches are better alternatives for multi-parametric updating...... with a non-conjugating formulation. The results in this paper show the influence on the time dependent updated reliability when non-parametric and classical Bayesian approaches are used. Further, the influence on the reliability of the number of updated parameters is illustrated....
Non-parametric reconstruction of the galaxy-lens in PG1115+080
Saha, P; Saha, Prasenjit; Williams, Liliya L. R.
1997-01-01
We describe a new, non-parametric, method for reconstructing lensing mass distributions in multiple-image systems, and apply it to PG1115, for which time delays have recently been measured. It turns out that the image positions and the ratio of time delays between different pairs of images constrain the mass distribution in a linear fashion. Since observational errors on image positions and time delay ratios are constantly improving, we use these data as a rigid constraint in our modelling. In addition, we require the projected mass distributions to be inversion-symmetric and to have inward-pointing density gradients. With these realistic yet non-restrictive conditions it is very easy to produce mass distributions that fit the data precisely. We then present models, for $H_0=42$, 63 and 84 \\kmsmpc, that in each case minimize mass-to-light variations while strictly obeying the lensing constraints. (Only a very rough light distribution is available at present.) All three values of $H_0$ are consistent with the ...
DEFF Research Database (Denmark)
Tan, Qihua; Zhao, J H; Iachine, I
2004-01-01
This report investigates the power issue in applying the non-parametric linkage analysis of affected sib-pairs (ASP) [Kruglyak and Lander, 1995: Am J Hum Genet 57:439-454] to localize genes that contribute to human longevity using long-lived sib-pairs. Data were simulated by introducing a recently...... developed statistical model for measuring marker-longevity associations [Yashin et al., 1999: Am J Hum Genet 65:1178-1193], enabling direct power comparison between linkage and association approaches. The non-parametric linkage (NPL) scores estimated in the region harboring the causal allele are evaluated...... in case of a dominant effect. Although the power issue may depend heavily on the true genetic nature in maintaining survival, our study suggests that results from small-scale sib-pair investigations should be referred with caution, given the complexity of human longevity....
On The Robustness of z=0-1 Galaxy Size Measurements Through Model and Non-Parametric Fits
Mosleh, Moein; Franx, Marijn
2013-01-01
We present the size-stellar mass relations of nearby (z=0.01-0.02) SDSS galaxies, for samples selected by color, morphology, Sersic index n, and specific star formation rate. Several commonly-employed size measurement techniques are used, including single Sersic fits, two-component Sersic models and a non-parametric method. Through simple simulations we show that the non-parametric and two-component Sersic methods provide the most robust effective radius measurements, while those based on single Sersic profiles are often overestimates, especially for massive red/early-type galaxies. Using our robust sizes, we show that for all sub-samples, the mass-size relations are shallow at low stellar masses and steepen above ~3-4 x 10^{10}\\Msun. The mass-size relations for galaxies classified as late-type, low-n, and star-forming are consistent with each other, while blue galaxies follow a somewhat steeper relation. The mass-size relations of early-type, high-n, red, and quiescent galaxies all agree with each other but ...
Statistical methods for environmental pollution monitoring
Energy Technology Data Exchange (ETDEWEB)
Gilbert, R.O.
1987-01-01
The application of statistics to environmental pollution monitoring studies requires a knowledge of statistical analysis methods particularly well suited to pollution data. This book fills that need by providing sampling plans, statistical tests, parameter estimation procedure techniques, and references to pertinent publications. Most of the statistical techniques are relatively simple, and examples, exercises, and case studies are provided to illustrate procedures. The book is logically divided into three parts. Chapters 1, 2, and 3 are introductory chapters. Chapters 4 through 10 discuss field sampling designs and Chapters 11 through 18 deal with a broad range of statistical analysis procedures. Some statistical techniques given here are not commonly seen in statistics book. For example, see methods for handling correlated data (Sections 4.5 and 11.12), for detecting hot spots (Chapter 10), and for estimating a confidence interval for the mean of a lognormal distribution (Section 13.2). Also, Appendix B lists a computer code that estimates and tests for trends over time at one or more monitoring stations using nonparametric methods (Chapters 16 and 17). Unfortunately, some important topics could not be included because of their complexity and the need to limit the length of the book. For example, only brief mention could be made of time series analysis using Box-Jenkins methods and of kriging techniques for estimating spatial and spatial-time patterns of pollution, although multiple references on these topics are provided. Also, no discussion of methods for assessing risks from environmental pollution could be included.
Statistical Methods for Environmental Pollution Monitoring
Energy Technology Data Exchange (ETDEWEB)
Gilbert, Richard O. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
1987-01-01
The application of statistics to environmental pollution monitoring studies requires a knowledge of statistical analysis methods particularly well suited to pollution data. This book fills that need by providing sampling plans, statistical tests, parameter estimation procedure techniques, and references to pertinent publications. Most of the statistical techniques are relatively simple, and examples, exercises, and case studies are provided to illustrate procedures. The book is logically divided into three parts. Chapters 1, 2, and 3 are introductory chapters. Chapters 4 through 10 discuss field sampling designs and Chapters 11 through 18 deal with a broad range of statistical analysis procedures. Some statistical techniques given here are not commonly seen in statistics book. For example, see methods for handling correlated data (Sections 4.5 and 11.12), for detecting hot spots (Chapter 10), and for estimating a confidence interval for the mean of a lognormal distribution (Section 13.2). Also, Appendix B lists a computer code that estimates and tests for trends over time at one or more monitoring stations using nonparametric methods (Chapters 16 and 17). Unfortunately, some important topics could not be included because of their complexity and the need to limit the length of the book. For example, only brief mention could be made of time series analysis using Box-Jenkins methods and of kriging techniques for estimating spatial and spatial-time patterns of pollution, although multiple references on these topics are provided. Also, no discussion of methods for assessing risks from environmental pollution could be included.
ABOUT THE METHODOLOGY OF STATISTICAL METHODS
Orlov A. I.
2014-01-01
The purpose of the article - to justify the need to develop the methodology of statistical methods as an independent scientific direction. The models of mathematician and applied specialist are presented. We have obtained the conclusions on teaching and research and discussed five major unsolved problems of statistical methods: the effect of deviations from the traditional prerequisites; use asymptotic results for finite sample sizes; selecting one of the many specific tests for the hypothesi...
Directory of Open Access Journals (Sweden)
Saeed Banihashemi
2015-12-01
Full Text Available In line with the growing global trend toward energy efficiency in buildings, this paper aims to first; investigate the energy performance of double-glazed windows in different climates and second; analyze the most dominant used parametric and non-parametric tests in dimension reduction for simulating this component. A four-story building representing the conventional type of residential apartments for four climates of cold, temperate, hot-arid and hot-humid was selected for simulation. 10 variables of U-factor, SHGC, emissivity, visible transmittance, monthly average dry bulb temperature, monthly average percent humidity, monthly average wind speed, monthly average direct solar radiation, monthly average diffuse solar radiation and orientation constituted the parameters considered in the calculation of cooling and heating loads of the case. Design of Experiment and Principal Component Analysis methods were applied to find the most significant factors and reduction dimension of initial variables. It was observed that in two climates of temperate and hot-arid, using double glazed windows was beneficial in both cold and hot months whereas in cold and hot-humid climates where heating and cooling loads are dominant respectively, they were advantageous in only those dominant months. Furthermore, an inconsistency was revealed between parametric and non-parametric tests in terms of identifying the most significant variables.
Modern statistical methods in respiratory medicine.
Wolfe, Rory; Abramson, Michael J
2014-01-01
Statistics sits right at the heart of scientific endeavour in respiratory medicine and many other disciplines. In this introductory article, some key epidemiological concepts such as representativeness, random sampling, association and causation, and confounding are reviewed. A brief introduction to basic statistics covering topics such as frequentist methods, confidence intervals, hypothesis testing, P values and Type II error is provided. Subsequent articles in this series will cover some modern statistical methods including regression models, analysis of repeated measures, causal diagrams, propensity scores, multiple imputation, accounting for measurement error, survival analysis, risk prediction, latent class analysis and meta-analysis.
Shi, Yang; Chinnaiyan, Arul M; Jiang, Hui
2015-07-01
High-throughput sequencing of transcriptomes (RNA-Seq) has become a powerful tool to study gene expression. Here we present an R package, rSeqNP, which implements a non-parametric approach to test for differential expression and splicing from RNA-Seq data. rSeqNP uses permutation tests to access statistical significance and can be applied to a variety of experimental designs. By combining information across isoforms, rSeqNP is able to detect more differentially expressed or spliced genes from RNA-Seq data. The R package with its source code and documentation are freely available at http://www-personal.umich.edu/∼jianghui/rseqnp/. jianghui@umich.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Van Steenbergen, N.; Willems, P.
2012-04-01
Reliable flood forecasts are the most important non-structural measures to reduce the impact of floods. However flood forecasting systems are subject to uncertainty originating from the input data, model structure and model parameters of the different hydraulic and hydrological submodels. To quantify this uncertainty a non-parametric data-based approach has been developed. This approach analyses the historical forecast residuals (differences between the predictions and the observations at river gauging stations) without using a predefined statistical error distribution. Because the residuals are correlated with the value of the forecasted water level and the lead time, the residuals are split up into discrete classes of simulated water levels and lead times. For each class, percentile values are calculated of the model residuals and stored in a 'three dimensional error' matrix. By 3D interpolation in this error matrix, the uncertainty in new forecasted water levels can be quantified. In addition to the quantification of the uncertainty, the communication of this uncertainty is equally important. The communication has to be done in a consistent way, reducing the chance of misinterpretation. Also, the communication needs to be adapted to the audience; the majority of the larger public is not interested in in-depth information on the uncertainty on the predicted water levels, but only is interested in information on the likelihood of exceedance of certain alarm levels. Water managers need more information, e.g. time dependent uncertainty information, because they rely on this information to undertake the appropriate flood mitigation action. There are various ways in presenting uncertainty information (numerical, linguistic, graphical, time (in)dependent, etc.) each with their advantages and disadvantages for a specific audience. A useful method to communicate uncertainty of flood forecasts is by probabilistic flood mapping. These maps give a representation of the
Non-parametric tests of productive efficiency with errors-in-variables
Kuosmanen, T.K.; Post, T.; Scholtes, S.
2007-01-01
We develop a non-parametric test of productive efficiency that accounts for errors-in-variables, following the approach of Varian. [1985. Nonparametric analysis of optimizing behavior with measurement error. Journal of Econometrics 30(1/2), 445-458]. The test is based on the general Pareto-Koopmans
DEFF Research Database (Denmark)
Ramirez, José Rangel; Sørensen, John Dalsgaard
2011-01-01
This work illustrates the updating and incorporation of information in the assessment of fatigue reliability for offshore wind turbine. The new information, coming from external and condition monitoring can be used to direct updating of the stochastic variables through a non-parametric Bayesian u...
Non-parametric production analysis of pesticides use in the Netherlands
Oude Lansink, A.G.J.M.; Silva, E.
2004-01-01
Many previous empirical studies on the productivity of pesticides suggest that pesticides are under-utilized in agriculture despite the general held believe that these inputs are substantially over-utilized. This paper uses data envelopment analysis (DEA) to calculate non-parametric measures of the
Agasisti, Tommaso
2011-01-01
The objective of this paper is an efficiency analysis concerning higher education systems in European countries. Data have been extracted from OECD data-sets (Education at a Glance, several years), using a non-parametric technique--data envelopment analysis--to calculate efficiency scores. This paper represents the first attempt to conduct such an…
Low default credit scoring using two-class non-parametric kernel density estimation
CSIR Research Space (South Africa)
Rademeyer, E
2016-12-01
Full Text Available This paper investigates the performance of two-class classification credit scoring data sets with low default ratios. The standard two-class parametric Gaussian and non-parametric Parzen classifiers are extended, using Bayes’ rule, to include either...
Measuring the influence of networks on transaction costs using a non-parametric regression technique
DEFF Research Database (Denmark)
Henningsen, Géraldine; Henningsen, Arne; Henning, Christian H.C.A.
. We empirically analyse the effect of networks on productivity using a cross-validated local linear non-parametric regression technique and a data set of 384 farms in Poland. Our empirical study generally supports our hypothesis that networks affect productivity. Large and dense trading networks...
A non-parametric 2D deformable template classifier
DEFF Research Database (Denmark)
Schultz, Nette; Nielsen, Allan Aasbjerg; Conradsen, Knut;
2005-01-01
We introduce an interactive segmentation method for a sea floor survey. The method is based on a deformable template classifier and is developed to segment data from an echo sounder post-processor called RoxAnn. RoxAnn collects two different measures for each observation point, and in this 2D...... feature space the ship-master will be able to interactively define a segmentation map, which is refined and optimized by the deformable template algorithms. The deformable templates are defined as two-dimensional vector-cycles. Local random transformations are applied to the vector-cycles, and stochastic...... relaxation in a Bayesian scheme is used. In the Bayesian likelihood a class density function and its estimate hereof is introduced, which is designed to separate the feature space. The method is verified on data collected in Øresund, Scandinavia. The data come from four geographically different areas. Two...
Spatial analysis statistics, visualization, and computational methods
Oyana, Tonny J
2015-01-01
An introductory text for the next generation of geospatial analysts and data scientists, Spatial Analysis: Statistics, Visualization, and Computational Methods focuses on the fundamentals of spatial analysis using traditional, contemporary, and computational methods. Outlining both non-spatial and spatial statistical concepts, the authors present practical applications of geospatial data tools, techniques, and strategies in geographic studies. They offer a problem-based learning (PBL) approach to spatial analysis-containing hands-on problem-sets that can be worked out in MS Excel or ArcGIS-as well as detailed illustrations and numerous case studies. The book enables readers to: Identify types and characterize non-spatial and spatial data Demonstrate their competence to explore, visualize, summarize, analyze, optimize, and clearly present statistical data and results Construct testable hypotheses that require inferential statistical analysis Process spatial data, extract explanatory variables, conduct statisti...
Workshop on Analytical Methods in Statistics
Jurečková, Jana; Maciak, Matúš; Pešta, Michal
2017-01-01
This volume collects authoritative contributions on analytical methods and mathematical statistics. The methods presented include resampling techniques; the minimization of divergence; estimation theory and regression, eventually under shape or other constraints or long memory; and iterative approximations when the optimal solution is difficult to achieve. It also investigates probability distributions with respect to their stability, heavy-tailness, Fisher information and other aspects, both asymptotically and non-asymptotically. The book not only presents the latest mathematical and statistical methods and their extensions, but also offers solutions to real-world problems including option pricing. The selected, peer-reviewed contributions were originally presented at the workshop on Analytical Methods in Statistics, AMISTAT 2015, held in Prague, Czech Republic, November 10-13, 2015.
Non-parametric causal inference for bivariate time series
McCracken, James M
2015-01-01
We introduce new quantities for exploratory causal inference between bivariate time series. The quantities, called penchants and leanings, are computationally straightforward to apply, follow directly from assumptions of probabilistic causality, do not depend on any assumed models for the time series generating process, and do not rely on any embedding procedures; these features may provide a clearer interpretation of the results than those from existing time series causality tools. The penchant and leaning are computed based on a structured method for computing probabilities.
[Pathogenesis of temporomandibular dysfunction. II. Statistical method].
Vágó, P
1989-08-01
The variables of the epidemiologic assessments concerned with the aetiology of the mandible joint disfunction were examined in the course of statistical analyses, in general, in their pairwise connections and possibly a multi-variable linear regression calculation was employed. In the course of the examination, for establishing the linear, empirically tested model of the aetiology of the mandible joint disfunction a new type statistical method, the LISREL (Linear Structural Relationship) method was employed. An advantage of this assessment consists in that not only observed variables may figure as the variables of the structural equation but also latent variables which cannot be observed but it is supposable that they are factors of the observed variables. This statistical method is described in closer details in the article in connection with the forming of the aetiological model.
Danielski, C; Tinetti, G
2013-01-01
The NASA Kepler mission is delivering groundbreaking results, with an increasing number of Earth-sized and moon-sized objects been discovered. A high photometric precision can be reached only through a thorough removal of the stellar activity and the instrumental systematics. We have explored here the possibility of using non-parametric methods to analyse the Simple Aperture Photometry data observed by the Kepler mission. We focused on a sample of stellar light curves with different effective temperatures and flux modulations, and we found that Gaussian Processes-based techniques can very effectively correct the instrumental systematics along with the long-term stellar activity. Our method can disentangle astrophysical features (events), such as planetary transits, flares or general sudden variations in the intensity, from the star signal and it is very efficient as it requires only a few training iterations of the Gaussian Process model. The results obtained show the potential of our method to isolate the ma...
Statistical methods for spatio-temporal systems
Finkenstadt, Barbel
2006-01-01
Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will promote advances in research and a greater understanding between the mechanistic and the statistical modeling communities.Contributed by leading researchers in the field, each self-contained chapter starts with an introduction of the topic and progresses to recent research results. Presenting specific examples of epidemic data of bovine tuberculosis, gastroenteric disease, and the U.K. foot-and-mouth outbreak, the first chapter uses stochastic models, such as point process models, to provide the probabilistic backbone that facilitates statistical inference from data. The next chapter discusses the critical issue of modeling random growth objects in diverse biological systems, such as bacteria colonies, tumors, and plant populations. The subsequent chapter examines data transformation tools using examples from ecology and air quality data, followed by a chapter on space-time co...
Statistical Methods for Stochastic Differential Equations
Kessler, Mathieu; Sorensen, Michael
2012-01-01
The seventh volume in the SemStat series, Statistical Methods for Stochastic Differential Equations presents current research trends and recent developments in statistical methods for stochastic differential equations. Written to be accessible to both new students and seasoned researchers, each self-contained chapter starts with introductions to the topic at hand and builds gradually towards discussing recent research. The book covers Wiener-driven equations as well as stochastic differential equations with jumps, including continuous-time ARMA processes and COGARCH processes. It presents a sp
A Non-Parametric and Entropy Based Analysis of the Relationship between the VIX and S&P 500
Directory of Open Access Journals (Sweden)
Abhay K. Singh
2013-10-01
Full Text Available This paper features an analysis of the relationship between the S&P 500 Index and the VIX using daily data obtained from the CBOE website and SIRCA (The Securities Industry Research Centre of the Asia Pacific. We explore the relationship between the S&P 500 daily return series and a similar series for the VIX in terms of a long sample drawn from the CBOE from 1990 to mid 2011 and a set of returns from SIRCA’s TRTH datasets from March 2005 to-date. This shorter sample, which captures the behavior of the new VIX, introduced in 2003, is divided into four sub-samples which permit the exploration of the impact of the Global Financial Crisis. We apply a series of non-parametric based tests utilizing entropy based metrics. These suggest that the PDFs and CDFs of these two return distributions change shape in various subsample periods. The entropy and MI statistics suggest that the degree of uncertainty attached to these distributions changes through time and using the S&P 500 return as the dependent variable, that the amount of information obtained from the VIX changes with time and reaches a relative maximum in the most recent period from 2011 to 2012. The entropy based non-parametric tests of the equivalence of the two distributions and their symmetry all strongly reject their respective nulls. The results suggest that parametric techniques do not adequately capture the complexities displayed in the behavior of these series. This has practical implications for hedging utilizing derivatives written on the VIX.
Pestman, Wiebe R
2009-01-01
This textbook provides a broad and solid introduction to mathematical statistics, including the classical subjects hypothesis testing, normal regression analysis, and normal analysis of variance. In addition, non-parametric statistics and vectorial statistics are considered, as well as applications of stochastic analysis in modern statistics, e.g., Kolmogorov-Smirnov testing, smoothing techniques, robustness and density estimation. For students with some elementary mathematical background. With many exercises. Prerequisites from measure theory and linear algebra are presented.
Applying statistical methods to text steganography
Nechta, Ivan
2011-01-01
This paper presents a survey of text steganography methods used for hid- ing secret information inside some covertext. Widely known hiding techniques (such as translation based steganography, text generating and syntactic embed- ding) and detection are considered. It is shown that statistical analysis has an important role in text steganalysis.
Statistical search methods for lotsizing problems
M. Salomon (Marc); R. Kuik (Roelof); L.N. van Wassenhove (Luk)
1993-01-01
textabstractThis paper reports on our experiments with statistical search methods for solving lotsizing problems in production planning. In lotsizing problems the main objective is to generate a minimum cost production and inventory schedule, such that (i) customer demand is satisfied, and (ii) capa
Energy Technology Data Exchange (ETDEWEB)
Lozano, Sebastian; Gutierrez, Ester [University of Seville, E.S.I., Department of Industrial Management, Camino de los Descubrimientos, s/n, 41092 Sevilla (Spain)
2008-07-15
In this paper, a non-parametric approach based in Data Envelopment Analysis (DEA) is proposed as an alternative to the Kaya identity (a.k.a ImPACT). This Frontier Method identifies and extends existing best practices. Population and GDP are considered as input and output, respectively. Both primary energy consumption and Greenhouse Gas (GHG) emissions are considered as undesirable outputs. Several Linear Programming models are formulated with different aims, namely: (a) determine efficiency levels, (b) estimate maximum GDP compatible with given levels of population, energy intensity and carbonization intensity, and (c) estimate the minimum level of GHG emissions compatible with given levels of population, GDP, energy intensity or carbonization index. The United States of America case is used as illustration of the proposed approach. (author)
Li, Xiao-Dong; Lv, Mang-Mang; Ho, John K. L.
2016-07-01
In this article, two adaptive iterative learning control (ILC) algorithms are presented for nonlinear continuous systems with non-parametric uncertainties. Unlike general ILC techniques, the proposed adaptive ILC algorithms allow that both the initial error at each iteration and the reference trajectory are iteration-varying in the ILC process, and can achieve non-repetitive trajectory tracking beyond a small initial time interval. Compared to the neural network or fuzzy system-based adaptive ILC schemes and the classical ILC methods, in which the number of iterative variables is generally larger than or equal to the number of control inputs, the first adaptive ILC algorithm proposed in this paper uses just two iterative variables, while the second even uses a single iterative variable provided that some bound information on system dynamics is known. As a result, the memory space in real-time ILC implementations is greatly reduced.
Estimating Financial Risk Measures for Futures Positions:A Non-Parametric Approach
Cotter, John; dowd, kevin
2011-01-01
This paper presents non-parametric estimates of spectral risk measures applied to long and short positions in 5 prominent equity futures contracts. It also compares these to estimates of two popular alternative measures, the Value-at-Risk (VaR) and Expected Shortfall (ES). The spectral risk measures are conditioned on the coefficient of absolute risk aversion, and the latter two are conditioned on the confidence level. Our findings indicate that all risk measures increase dramatically and the...
The statistical process control methods - SPC
Directory of Open Access Journals (Sweden)
Floreková Ľubica
1998-03-01
Full Text Available Methods of statistical evaluation of quality SPC (item 20 of the documentation system of quality control of ISO norm, series 900 of various processes, products and services belong amongst basic qualitative methods that enable us to analyse and compare data pertaining to various quantitative parameters. Also they enable, based on the latter, to propose suitable interventions with the aim of improving these processes, products and services. Theoretical basis and applicatibily of the principles of the: - diagnostics of a cause and effects, - Paret analysis and Lorentz curve, - number distribution and frequency curves of random variable distribution, - Shewhart regulation charts, are presented in the contribution.
Non-parametric determination of H and He interstellar fluxes from cosmic-ray data
Ghelfi, A.; Barao, F.; Derome, L.; Maurin, D.
2016-06-01
Context. Top-of-atmosphere (TOA) cosmic-ray (CR) fluxes from satellites and balloon-borne experiments are snapshots of the solar activity imprinted on the interstellar (IS) fluxes. Given a series of snapshots, the unknown IS flux shape and the level of modulation (for each snapshot) can be recovered. Aims: We wish (i) to provide the most accurate determination of the IS H and He fluxes from TOA data alone; (ii) to obtain the associated modulation levels (and uncertainties) while fully accounting for the correlations with the IS flux uncertainties; and (iii) to inspect whether the minimal force-field approximation is sufficient to explain all the data at hand. Methods: Using H and He TOA measurements, including the recent high-precision AMS, BESS-Polar, and PAMELA data, we performed a non-parametric fit of the IS fluxes JISH,~He and modulation level φi for each data-taking period. We relied on a Markov chain Monte Carlo (MCMC) engine to extract the probability density function and correlations (hence the credible intervals) of the sought parameters. Results: Although H and He are the most abundant and best measured CR species, several datasets had to be excluded from the analysis because of inconsistencies with other measurements. From the subset of data passing our consistency cut, we provide ready-to-use best-fit and credible intervals for the H and He IS fluxes from MeV/n to PeV/n energy (with a relative precision in the range [ 2-10% ] at 1σ). Given the strong correlation between JIS and φi parameters, the uncertainties on JIS translate into Δφ ≈ ± 30 MV (at 1σ) for all experiments. We also find that the presence of 3He in He data biases φ towards higher φ values by ~30 MV. The force-field approximation, despite its limitation, gives an excellent (χ2/d.o.f. = 1.02) description of the recent high-precision TOA H and He fluxes. Conclusions: The analysis must be extended to different charge species and more realistic modulation models. It would benefit
Statistical methods for assessment of blend homogeneity
DEFF Research Database (Denmark)
Madsen, Camilla
2002-01-01
In this thesis the use of various statistical methods to address some of the problems related to assessment of the homogeneity of powder blends in tablet production is discussed. It is not straight forward to assess the homogeneity of a powder blend. The reason is partly that in bulk materials......, it is shown how to set up parametric acceptance criteria for the batch that gives a high confidence that future samples with a probability larger than a specified value will pass the USP threeclass criteria. Properties and robustness of proposed changes to the USP test for content uniformity are investigated...
Statistical methods in credit risk management
Directory of Open Access Journals (Sweden)
Ljiljanka Kvesić
2012-12-01
Full Text Available Successful banks base their operations on the principles of liquidity, profitability and safety. Therefore, the correct assessment of the ability of a loan applicant to carry out certain obligations is of crucial importance for the functioning of a bank. In the past few decades several credit scoring models have been developed to provide support to credit analysts in the assessment of a loan applicant. This paper presents three statistical methods that are used for this purpose in the area of credit risk management: logistical regression, discriminatory analysis and survival analysis. Their implementation in the banking sector was motivated to a great extent by the development and application of information and communication technologies. This paper aims to point out the most important theoretical aspects of these methods, but also to actualise the need for the development and application of the credit scoring model in Croatian banking practice.
Statistical Methods in Phylogenetic and Evolutionary Inferences
Directory of Open Access Journals (Sweden)
Luigi Bertolotti
2013-05-01
Full Text Available Molecular instruments are the most accurate methods in organisms’identification and characterization. Biologists are often involved in studies where the main goal is to identify relationships among individuals. In this framework, it is very important to know and apply the most robust approaches to infer correctly these relationships, allowing the right conclusions about phylogeny. In this review, we will introduce the reader to the most used statistical methods in phylogenetic analyses, the Maximum Likelihood and the Bayesian approaches, considering for simplicity only analyses regardingDNA sequences. Several studieswill be showed as examples in order to demonstrate how the correct phylogenetic inference can lead the scientists to highlight very peculiar features in pathogens biology and evolution.
Kerschbamer, Rudolf
2015-05-01
This paper proposes a geometric delineation of distributional preference types and a non-parametric approach for their identification in a two-person context. It starts with a small set of assumptions on preferences and shows that this set (i) naturally results in a taxonomy of distributional archetypes that nests all empirically relevant types considered in previous work; and (ii) gives rise to a clean experimental identification procedure - the Equality Equivalence Test - that discriminates between archetypes according to core features of preferences rather than properties of specific modeling variants. As a by-product the test yields a two-dimensional index of preference intensity.
Application of pedagogy reflective in statistical methods course and practicum statistical methods
Julie, Hongki
2017-08-01
Subject Elementary Statistics, Statistical Methods and Statistical Methods Practicum aimed to equip students of Mathematics Education about descriptive statistics and inferential statistics. The students' understanding about descriptive and inferential statistics were important for students on Mathematics Education Department, especially for those who took the final task associated with quantitative research. In quantitative research, students were required to be able to present and describe the quantitative data in an appropriate manner, to make conclusions from their quantitative data, and to create relationships between independent and dependent variables were defined in their research. In fact, when students made their final project associated with quantitative research, it was not been rare still met the students making mistakes in the steps of making conclusions and error in choosing the hypothetical testing process. As a result, they got incorrect conclusions. This is a very fatal mistake for those who did the quantitative research. There were some things gained from the implementation of reflective pedagogy on teaching learning process in Statistical Methods and Statistical Methods Practicum courses, namely: 1. Twenty two students passed in this course and and one student did not pass in this course. 2. The value of the most accomplished student was A that was achieved by 18 students. 3. According all students, their critical stance could be developed by them, and they could build a caring for each other through a learning process in this course. 4. All students agreed that through a learning process that they undergo in the course, they can build a caring for each other.
Scaling of preferential flow in biopores by parametric or non parametric transfer functions
Zehe, E.; Hartmann, N.; Klaus, J.; Palm, J.; Schroeder, B.
2009-04-01
finally assign the measured hydraulic capacities to these pores. By combining this population of macropores with observed data on soil hydraulic properties we obtain a virtual reality. Flow and transport is simulated for different rainfall forcings comparing two models, Hydrus 3d and Catflow. The simulated cumulative travel depths distributions for different forcings will be linked to the cumulative depth distribution of connected flow paths. The latter describes the fraction of connected paths - where flow resistance is always below a selected threshold that links the surface to a certain critical depth. Systematic variation of the average number of macropores and their depth distributions will show whether a clear link between the simulated travel depths distributions and the depth distribution of connected paths may be identified. The third essential step is to derive a non parametric transfer function that predicts travel depth distributions of tracers and on the long term pesticides based on easy-to-assess subsurface characteristics (mainly density and depth distribution of worm burrows, soil matrix properties), initial conditions and rainfall forcing. Such a transfer function is independent of scale ? as long as we stay in the same ensemble i.e. worm population and soil properties stay the same. Shipitalo, M.J. and Butt, K.R. (1999): Occupancy and geometrical properties of Lumbricus terrestris L. burrows affecting infiltration. Pedobiologia 43:782-794 Zehe E, and Fluehler H. (2001b): Slope scale distribution of flow patterns in soil profiles. J. Hydrol. 247: 116-132.
On two methods of statistical image analysis
Missimer, J; Knorr, U; Maguire, RP; Herzog, H; Seitz, RJ; Tellman, L; Leenders, KL
1999-01-01
The computerized brain atlas (CBA) and statistical parametric mapping (SPM) are two procedures for voxel-based statistical evaluation of PET activation studies. Each includes spatial standardization of image volumes, computation of a statistic, and evaluation of its significance. In addition, smooth
Cliff´s Delta Calculator: A non-parametric effect size program for two groups of observations
Directory of Open Access Journals (Sweden)
Guillermo Macbeth
2011-05-01
Full Text Available The Cliff´s Delta statistic is an effect size measure that quantifies the amount of difference between two non-parametric variables beyond p-values interpretation. This measure can be understood as a useful complementary analysis for the corresponding hypothesis testing. During the last two decades the use of effect size measures has been strongly encouraged by methodologists and leading institutions of behavioral sciences. The aim of this contribution is to introduce the Cliff´s Delta Calculator software that performs such analysis and offers some interpretation tips. Differences and similarities with the parametric case are analysed and illustrated. The implementation of this free program is fully described and compared with other calculators. Alternative algorithmic approaches are mathematically analysed and a basic linear algebra proof of its equivalence is formally presented. Two worked examples in cognitive psychology are commented. A visual interpretation of Cliff´s Delta is suggested. Availability, installation and applications of the program are presented and discussed.
The Monte Carlo method the method of statistical trials
Shreider, YuA
1966-01-01
The Monte Carlo Method: The Method of Statistical Trials is a systematic account of the fundamental concepts and techniques of the Monte Carlo method, together with its range of applications. Some of these applications include the computation of definite integrals, neutron physics, and in the investigation of servicing processes. This volume is comprised of seven chapters and begins with an overview of the basic features of the Monte Carlo method and typical examples of its application to simple problems in computational mathematics. The next chapter examines the computation of multi-dimensio
Comparison of three Statistical Classification Techniques for Maser Identification
Manning, Ellen M; Ellingsen, Simon P; Breen, Shari L; Chen, Xi; Humphries, Melissa
2016-01-01
We applied three statistical classification techniques - linear discriminant analysis (LDA), logistic regression and random forests - to three astronomical datasets associated with searches for interstellar masers. We compared the performance of these methods in identifying whether specific mid-infrared or millimetre continuum sources are likely to have associated interstellar masers. We also discuss the ease, or otherwise, with which the results of each classification technique can be interpreted. Non-parametric methods have the potential to make accurate predictions when there are complex relationships between critical parameters. We found that for the small datasets the parametric methods logistic regression and LDA performed best, for the largest dataset the non-parametric method of random forests performed with comparable accuracy to parametric techniques, rather than any significant improvement. This suggests that at least for the specific examples investigated here accuracy of the predictions obtained ...
Statistical methods for astronomical data analysis
Chattopadhyay, Asis Kumar
2014-01-01
This book introduces “Astrostatistics” as a subject in its own right with rewarding examples, including work by the authors with galaxy and Gamma Ray Burst data to engage the reader. This includes a comprehensive blending of Astrophysics and Statistics. The first chapter’s coverage of preliminary concepts and terminologies for astronomical phenomenon will appeal to both Statistics and Astrophysics readers as helpful context. Statistics concepts covered in the book provide a methodological framework. A unique feature is the inclusion of different possible sources of astronomical data, as well as software packages for converting the raw data into appropriate forms for data analysis. Readers can then use the appropriate statistical packages for their particular data analysis needs. The ideas of statistical inference discussed in the book help readers determine how to apply statistical tests. The authors cover different applications of statistical techniques already developed or specifically introduced for ...
Robust non-parametric one-sample tests for the analysis of recurrent events.
Rebora, Paola; Galimberti, Stefania; Valsecchi, Maria Grazia
2010-12-30
One-sample non-parametric tests are proposed here for inference on recurring events. The focus is on the marginal mean function of events and the basis for inference is the standardized distance between the observed and the expected number of events under a specified reference rate. Different weights are considered in order to account for various types of alternative hypotheses on the mean function of the recurrent events process. A robust version and a stratified version of the test are also proposed. The performance of these tests was investigated through simulation studies under various underlying event generation processes, such as homogeneous and nonhomogeneous Poisson processes, autoregressive and renewal processes, with and without frailty effects. The robust versions of the test have been shown to be suitable in a wide variety of event generating processes. The motivating context is a study on gene therapy in a very rare immunodeficiency in children, where a major end-point is the recurrence of severe infections. Robust non-parametric one-sample tests for recurrent events can be useful to assess efficacy and especially safety in non-randomized studies or in epidemiological studies for comparison with a standard population.
MEASURING DARK MATTER PROFILES NON-PARAMETRICALLY IN DWARF SPHEROIDALS: AN APPLICATION TO DRACO
Energy Technology Data Exchange (ETDEWEB)
Jardel, John R.; Gebhardt, Karl [Department of Astronomy, The University of Texas, 2515 Speedway, Stop C1400, Austin, TX 78712-1205 (United States); Fabricius, Maximilian H.; Williams, Michael J. [Max-Planck Institut fuer extraterrestrische Physik, Giessenbachstrasse, D-85741 Garching bei Muenchen (Germany); Drory, Niv, E-mail: jardel@astro.as.utexas.edu [Instituto de Astronomia, Universidad Nacional Autonoma de Mexico, Avenida Universidad 3000, Ciudad Universitaria, C.P. 04510 Mexico D.F. (Mexico)
2013-02-15
We introduce a novel implementation of orbit-based (or Schwarzschild) modeling that allows dark matter density profiles to be calculated non-parametrically in nearby galaxies. Our models require no assumptions to be made about velocity anisotropy or the dark matter profile. The technique can be applied to any dispersion-supported stellar system, and we demonstrate its use by studying the Local Group dwarf spheroidal galaxy (dSph) Draco. We use existing kinematic data at larger radii and also present 12 new radial velocities within the central 13 pc obtained with the VIRUS-W integral field spectrograph on the 2.7 m telescope at McDonald Observatory. Our non-parametric Schwarzschild models find strong evidence that the dark matter profile in Draco is cuspy for 20 {<=} r {<=} 700 pc. The profile for r {>=} 20 pc is well fit by a power law with slope {alpha} = -1.0 {+-} 0.2, consistent with predictions from cold dark matter simulations. Our models confirm that, despite its low baryon content relative to other dSphs, Draco lives in a massive halo.
A Bayesian non-parametric Potts model with application to pre-surgical FMRI data.
Johnson, Timothy D; Liu, Zhuqing; Bartsch, Andreas J; Nichols, Thomas E
2013-08-01
The Potts model has enjoyed much success as a prior model for image segmentation. Given the individual classes in the model, the data are typically modeled as Gaussian random variates or as random variates from some other parametric distribution. In this article, we present a non-parametric Potts model and apply it to a functional magnetic resonance imaging study for the pre-surgical assessment of peritumoral brain activation. In our model, we assume that the Z-score image from a patient can be segmented into activated, deactivated, and null classes, or states. Conditional on the class, or state, the Z-scores are assumed to come from some generic distribution which we model non-parametrically using a mixture of Dirichlet process priors within the Bayesian framework. The posterior distribution of the model parameters is estimated with a Markov chain Monte Carlo algorithm, and Bayesian decision theory is used to make the final classifications. Our Potts prior model includes two parameters, the standard spatial regularization parameter and a parameter that can be interpreted as the a priori probability that each voxel belongs to the null, or background state, conditional on the lack of spatial regularization. We assume that both of these parameters are unknown, and jointly estimate them along with other model parameters. We show through simulation studies that our model performs on par, in terms of posterior expected loss, with parametric Potts models when the parametric model is correctly specified and outperforms parametric models when the parametric model in misspecified.
Kulmala, A; Tenhunen, M
2012-11-07
The signal of the dosimetric detector is generally dependent on the shape and size of the sensitive volume of the detector. In order to optimize the performance of the detector and reliability of the output signal the effect of the detector size should be corrected or, at least, taken into account. The response of the detector can be modelled using the convolution theorem that connects the system input (actual dose), output (measured result) and the effect of the detector (response function) by a linear convolution operator. We have developed the super-resolution and non-parametric deconvolution method for determination of the cylinder symmetric ionization chamber radial response function. We have demonstrated that the presented deconvolution method is able to determine the radial response for the Roos parallel plate ionization chamber with a better than 0.5 mm correspondence with the physical measures of the chamber. In addition, the performance of the method was proved by the excellent agreement between the output factors of the stereotactic conical collimators (4-20 mm diameter) measured by the Roos chamber, where the detector size is larger than the measured field, and the reference detector (diode). The presented deconvolution method has a potential in providing reference data for more accurate physical models of the ionization chamber as well as for improving and enhancing the performance of the detectors in specific dosimetric problems.
Bahrami, Sheyda; Shamsi, Mousa
2017-01-01
Functional magnetic resonance imaging (fMRI) is a popular method to probe the functional organization of the brain using hemodynamic responses. In this method, volume images of the entire brain are obtained with a very good spatial resolution and low temporal resolution. However, they always suffer from high dimensionality in the face of classification algorithms. In this work, we combine a support vector machine (SVM) with a self-organizing map (SOM) for having a feature-based classification by using SVM. Then, a linear kernel SVM is used for detecting the active areas. Here, we use SOM for feature extracting and labeling the datasets. SOM has two major advances: (i) it reduces dimension of data sets for having less computational complexity and (ii) it is useful for identifying brain regions with small onset differences in hemodynamic responses. Our non-parametric model is compared with parametric and non-parametric methods. We use simulated fMRI data sets and block design inputs in this paper and consider the contrast to noise ratio (CNR) value equal to 0.6 for simulated datasets. fMRI simulated dataset has contrast 1-4% in active areas. The accuracy of our proposed method is 93.63% and the error rate is 6.37%.
Institute of Scientific and Technical Information of China (English)
Yong Liu,; Wei Zou
2011-01-01
Extending the income dynamics approach in Quah （2003）, the present paper studies the enlarging income inequality in China over the past three decades from the viewpoint of rural-urban migration and economic transition. We establish non-parametric estimations of rural and urban income distribution functions in China, and aggregate a population- weighted, nationwide income distribution function taking into account rural-urban differences in technological progress and price indexes. We calculate 12 inequality indexes through non-parametric estimation to overcome the biases in existingparametric estimation and, therefore, provide more accurate measurement of income inequalitY. Policy implications have been drawn based on our research.
Non-parametric co-clustering of large scale sparse bipartite networks on the GPU
DEFF Research Database (Denmark)
Hansen, Toke Jansen; Mørup, Morten; Hansen, Lars Kai
2011-01-01
Co-clustering is a problem of both theoretical and practical importance, e.g., market basket analysis and collaborative filtering, and in web scale text processing. We state the co-clustering problem in terms of non-parametric generative models which can address the issue of estimating the number...... of row and column clusters from a hypothesis space of an infinite number of clusters. To reach large scale applications of co-clustering we exploit that parameter inference for co-clustering is well suited for parallel computing. We develop a generic GPU framework for efficient inference on large scale......-life large scale collaborative filtering data and web scale text corpora, demonstrating that latent mesoscale structures extracted by the co-clustering problem as formulated by the Infinite Relational Model (IRM) are consistent across consecutive runs with different initializations and also relevant...
Comparative Study of Parametric and Non-parametric Approaches in Fault Detection and Isolation
DEFF Research Database (Denmark)
Katebi, S.D.; Blanke, M.; Katebi, M.R.
This report describes a comparative study between two approaches to fault detection and isolation in dynamic systems. The first approach uses a parametric model of the system. The main components of such techniques are residual and signature generation for processing and analyzing. The second...... approach is non-parametric in the sense that the signature analysis is only dependent on the frequency or time domain information extracted directly from the input-output signals. Based on these approaches, two different fault monitoring schemes are developed where the feature extraction and fault decision...... algorithms employed are adopted from the template matching in pattern recognition. Extensive simulation studies are performed to demonstrate satisfactory performance of the proposed techniques. The advantages and disadvantages of each approach are discussed and analyzed....
Factors associated with malnutrition among tribal children in India: a non-parametric approach.
Debnath, Avijit; Bhattacharjee, Nairita
2014-06-01
The purpose of this study is to identify the determinants of malnutrition among the tribal children in India. The investigation is based on secondary data compiled from the National Family Health Survey-3. We used a classification and regression tree model, a non-parametric approach, to address the objective. Our analysis shows that breastfeeding practice, economic status, antenatal care of mother and women's decision-making autonomy are negatively associated with malnutrition among tribal children. We identify maternal malnutrition and urban concentration of household as the two risk factors for child malnutrition. The identified associated factors may be used for designing and targeting preventive programmes for malnourished tribal children. © The Author [2014]. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Farcomeni, Alessio; Serranti, Silvia; Bonifazi, Giuseppe
2008-01-01
Glass ceramic detection in glass recycling plants represents a still unsolved problem, as glass ceramic material looks like normal glass and is usually detected only by specialized personnel. The presence of glass-like contaminants inside waste glass products, resulting from both industrial and differentiated urban waste collection, increases process production costs and reduces final product quality. In this paper an innovative approach for glass ceramic recognition, based on the non-parametric analysis of infrared spectra, is proposed and investigated. The work was specifically addressed to the spectral classification of glass and glass ceramic fragments collected in an actual recycling plant from three different production lines: flat glass, colored container-glass and white container-glass. The analyses, carried out in the near and mid-infrared (NIR-MIR) spectral field (1280-4480 nm), show that glass ceramic and glass fragments can be recognized by applying a wavelet transform, with a small classification error. Moreover, a method for selecting only a small subset of relevant wavelength ratios is suggested, allowing the conduct of a fast recognition of the two classes of materials. The results show how the proposed approach can be utilized to develop a classification engine to be integrated inside a hardware and software sorting architecture for fast "on-line" ceramic glass recognition and separation.
Kumar, Upendra; Pal, Manoj Kumar
2012-01-01
Context-dependence in human cognition process is a well-established fact. Following this, we introduced the image segmentation method that can use context to classify a pixel on the basis of its membership to a particular object-class of the concerned image. In the broad methodological steps, each pixel was defined by its context window (CW) surrounding it the size of which was fixed heuristically. CW texture defined by the intensities of its pixels was convoluted with weights optimized through a non-parametric function supported by a backpropagation network. Result of convolution was used to classify them. The training data points (i.e., pixels) were carefully chosen to include all variety of contexts of types, i) points within the object, ii) points near the edge but inside the objects, iii) points at the border of the objects, iv) points near the edge but outside the objects, v) points near or at the edge of the image frame. Moreover the training data points were selected from all the images within image-d...
Directory of Open Access Journals (Sweden)
Rohin Anhal
2013-10-01
Full Text Available The aim of this paper is to examine the direction of causality between real GDP on the one hand and final energy and coal consumption on the other in India, for the period from 1970 to 2011. The methodology adopted is the non-parametric bootstrap procedure, which is used to construct the critical values for the hypothesis of causality. The results of the bootstrap tests show that for total energy consumption, there exists no causal relationship in either direction with GDP of India. However, if coal consumption is considered, we find evidence in support of unidirectional causality running from coal consumption to GDP. This clearly has important implications for the Indian economy. The most important implication is that curbing coal consumption in order to reduce carbon emissions would in turn have a limiting effect on economic growth. Our analysis contributes to the literature in three distinct ways. First, this is the first paper to use the bootstrap method to examine the growth-energy connection for the Indian economy. Second, we analyze data for the time period 1970 to 2011, thereby utilizing recently available data that has not been used by others. Finally, in contrast to the recently done studies, we adopt a disaggregated approach for the analysis of the growth-energy nexus by considering not only aggregate energy consumption, but coal consumption as well.
Link, J M; Alimonti, G; Anjos, J C; Arena, V; Barberis, S; Bediaga, I; Benussi, L; Bianco, S; Boca, G; Bonomi, G; Boschini, M; Butler, J N; Carrillo, S; Casimiro, E; Castromonte, C; Cawlfield, C; Cerutti, A; Cheung, H W K; Chiodini, G; Cho, K; Chung, Y S; Cinquini, L; Cuautle, E; Cumalat, J P; D'Angelo, P; Davenport, T F; De Miranda, J M; Di Corato, M; Dini, P; Dos Reis, A C; Edera, L; Engh, D; Erba, S; Fabbri, F L; Frisullo, V; Gaines, I; Garbincius, P H; Gardner, R; Garren, L A; Gianini, G; Gottschalk, E; Göbel, C; Handler, T; Hernández, H; Hosack, M; Inzani, P; Johns, W E; Kang, J S; Kasper, P H; Kim, D Y; Ko, B R; Kreymer, A E; Kryemadhi, A; Kutschke, R; Kwak, J W; Lee, K B; Leveraro, F; Liguori, G; Lopes-Pegna, D; Luiggi, E; López, A M; Machado, A A; Magnin, J; Malvezzi, S; Massafferri, A; Menasce, D; Merlo, M M; Mezzadri, M; Mitchell, R; Moroni, L; Méndez, H; Nehring, M; O'Reilly, B; Otalora, J; Pantea, D; Paris, A; Park, H; Pedrini, D; Pepe, I M; Polycarpo, E; Pontoglio, C; Prelz, F; Quinones, J; Rahimi, A; Ramírez, J E; Ratti, S P; Reyes, M; Riccardi, C; Rovere, M; Sala, S; Segoni, I; Sheaff, M; Sheldon, P D; Stenson, K; Sánchez-Hernández, A; Uribe, C; Vaandering, E W; Vitulo, P; Vázquez, F; Wang, M; Webster, M; Wilson, J R; Wiss, J; Yager, P M; Zallo, A; Zhang, Y
2007-01-01
Using a large sample of \\dpkkpi{} decays collected by the FOCUS photoproduction experiment at Fermilab, we present the first non-parametric analysis of the \\kpi{} amplitudes in \\dpkkpi{} decay. The technique is similar to the technique used for our non-parametric measurements of the \\krzmndk{} form factors. Although these results are in rough agreement with those of E687, we observe a wider S-wave contribution for the \\ksw{} contribution than the standard, PDG \\cite{pdg} Breit-Wigner parameterization. We have some weaker evidence for the existence of a new, D-wave component at low values of the $K^- \\pi^+$ mass.
Development of a Research Methods and Statistics Concept Inventory
Veilleux, Jennifer C.; Chapman, Kate M.
2017-01-01
Research methods and statistics are core courses in the undergraduate psychology major. To assess learning outcomes, it would be useful to have a measure that assesses research methods and statistical literacy beyond course grades. In two studies, we developed and provided initial validation results for a research methods and statistical knowledge…
Häme, Yrjö; Pollari, Mika
2012-01-01
A novel liver tumor segmentation method for CT images is presented. The aim of this work was to reduce the manual labor and time required in the treatment planning of radiofrequency ablation (RFA), by providing accurate and automated tumor segmentations reliably. The developed method is semi-automatic, requiring only minimal user interaction. The segmentation is based on non-parametric intensity distribution estimation and a hidden Markov measure field model, with application of a spherical shape prior. A post-processing operation is also presented to remove the overflow to adjacent tissue. In addition to the conventional approach of using a single image as input data, an approach using images from multiple contrast phases was developed. The accuracy of the method was validated with two sets of patient data, and artificially generated samples. The patient data included preoperative RFA images and a public data set from "3D Liver Tumor Segmentation Challenge 2008". The method achieved very high accuracy with the RFA data, and outperformed other methods evaluated with the public data set, receiving an average overlap error of 30.3% which represents an improvement of 2.3% points to the previously best performing semi-automatic method. The average volume difference was 23.5%, and the average, the RMS, and the maximum surface distance errors were 1.87, 2.43, and 8.09 mm, respectively. The method produced good results even for tumors with very low contrast and ambiguous borders, and the performance remained high with noisy image data.
Institute of Scientific and Technical Information of China (English)
金如锋
2011-01-01
候选基因关联研究中基因-基因、基因-环境交互作用的统计分析有利于揭示疾病的发生机制.本文针对病例对照设计的候选基因关联研究,综述交互作用的统计方法及其进展.交互作用的统计方法包括参数法和非参数法.参数法中最常用的为Logistic回归模型,非参数法主要是数据挖掘方法.有4类数据挖掘方法可用于候选基因关联研究,包括降维法、基于树的方法、模式识别法和贝叶斯法.本文对最常用且可靠的几种数据挖掘方法(多因子降维法、分类回归树、随机森林、贝叶斯上位效应关联图谱)的原理、分析过程和优缺点予以比较.参数法和非参数法分析交互作用时各有优缺点;低维数据的分析可采用参数法和非参数法,高维数据的分析则主要采用非参数法.随着基因分型技术的发展,可检测的SNP规模逐渐增大,使得非参数方法的应用越来越广.%Testing for gene-gene and gene-environment interactions in candidate gene association studies will help to reveal possible mechanisms underlying diseases. This article summarized the progress of statistical methods for testing interactions in candidate gene association studies based on case-control design. Parametric and non-parametric methods can be used to detect the interactions. Logistic regression is the most frequently used parametric method,and data mining techniques offer a variety of alternative non-parametric methods. Data mining techniques that can be applied in association studies consist of dimension reduction, tree-based approach, pattern recognition and Bayesian methods. Among alternative non-parametric methods we concentrated on the four methods which have become popular and are reliable for detection of interactions, including multifactor dimensionality reduction (MDR),classification and regression tree (CART), random forest, and Bayesian epistasis association mapping (BEAM). The principles
METHODS TO RESTRUCTURE THE STATISTICAL COMMUNITIES
Directory of Open Access Journals (Sweden)
Emilia TITAN
2005-12-01
Full Text Available In view of knowing the essence of phenomena it is necessary to perform statistical data processing operations. This allows for shifting from individual data to derived, synthetic indicators that highlight the essence of various phenomena. The high volume and diversity of processing operations presuppose developing plans of computerised data processing. To identify distinct and homogenous groups and classes it is necessary to realise well-pondered groupings and classifications that presuppose to comply with the requirements presented in the article.
Statistical models and methods for reliability and survival analysis
Couallier, Vincent; Huber-Carol, Catherine; Mesbah, Mounir; Huber -Carol, Catherine; Limnios, Nikolaos; Gerville-Reache, Leo
2013-01-01
Statistical Models and Methods for Reliability and Survival Analysis brings together contributions by specialists in statistical theory as they discuss their applications providing up-to-date developments in methods used in survival analysis, statistical goodness of fit, stochastic processes for system reliability, amongst others. Many of these are related to the work of Professor M. Nikulin in statistics over the past 30 years. The authors gather together various contributions with a broad array of techniques and results, divided into three parts - Statistical Models and Methods, Statistical
Statistical methods of estimating mining costs
Long, K.R.
2011-01-01
Until it was defunded in 1995, the U.S. Bureau of Mines maintained a Cost Estimating System (CES) for prefeasibility-type economic evaluations of mineral deposits and estimating costs at producing and non-producing mines. This system had a significant role in mineral resource assessments to estimate costs of developing and operating known mineral deposits and predicted undiscovered deposits. For legal reasons, the U.S. Geological Survey cannot update and maintain CES. Instead, statistical tools are under development to estimate mining costs from basic properties of mineral deposits such as tonnage, grade, mineralogy, depth, strip ratio, distance from infrastructure, rock strength, and work index. The first step was to reestimate "Taylor's Rule" which relates operating rate to available ore tonnage. The second step was to estimate statistical models of capital and operating costs for open pit porphyry copper mines with flotation concentrators. For a sample of 27 proposed porphyry copper projects, capital costs can be estimated from three variables: mineral processing rate, strip ratio, and distance from nearest railroad before mine construction began. Of all the variables tested, operating costs were found to be significantly correlated only with strip ratio.
Gugushvili, S.; Spreij, P.
2016-01-01
We consider the problem of non-parametric estimation of the deterministic dispersion coefficient of a linear stochastic differential equation based on discrete time observations on its solution. We take a Bayesian approach to the problem and under suitable regularity assumptions derive the posteror
Maydeu-Olivares, Albert
2005-01-01
Chernyshenko, Stark, Chan, Drasgow, and Williams (2001) investigated the fit of Samejima's logistic graded model and Levine's non-parametric MFS model to the scales of two personality questionnaires and found that the graded model did not fit well. We attribute the poor fit of the graded model to small amounts of multidimensionality present in…
Innovative statistical methods for public health data
Wilson, Jeffrey
2015-01-01
The book brings together experts working in public health and multi-disciplinary areas to present recent issues in statistical methodological development and their applications. This timely book will impact model development and data analyses of public health research across a wide spectrum of analysis. Data and software used in the studies are available for the reader to replicate the models and outcomes. The fifteen chapters range in focus from techniques for dealing with missing data with Bayesian estimation, health surveillance and population definition and implications in applied latent class analysis, to multiple comparison and meta-analysis in public health data. Researchers in biomedical and public health research will find this book to be a useful reference, and it can be used in graduate level classes.
Methods of contemporary mathematical statistical physics
2009-01-01
This volume presents a collection of courses introducing the reader to the recent progress with attention being paid to laying solid grounds and developing various basic tools. An introductory chapter on lattice spin models is useful as a background for other lectures of the collection. The topics include new results on phase transitions for gradient lattice models (with introduction to the techniques of the reflection positivity), stochastic geometry reformulation of classical and quantum Ising models, the localization/delocalization transition for directed polymers. A general rigorous framework for theory of metastability is presented and particular applications in the context of Glauber and Kawasaki dynamics of lattice models are discussed. A pedagogical account of several recently discussed topics in nonequilibrium statistical mechanics with an emphasis on general principles is followed by a discussion of kinetically constrained spin models that are reflecting important peculiar features of glassy dynamic...
Mathematical and statistical methods for multistatic imaging
Ammari, Habib; Jing, Wenjia; Kang, Hyeonbae; Lim, Mikyoung; Sølna, Knut; Wang, Han
2013-01-01
This book covers recent mathematical, numerical, and statistical approaches for multistatic imaging of targets with waves at single or multiple frequencies. The waves can be acoustic, elastic or electromagnetic. They are generated by point sources on a transmitter array and measured on a receiver array. An important problem in multistatic imaging is to quantify and understand the trade-offs between data size, computational complexity, signal-to-noise ratio, and resolution. Another fundamental problem is to have a shape representation well suited to solving target imaging problems from multistatic data. In this book the trade-off between resolution and stability when the data are noisy is addressed. Efficient imaging algorithms are provided and their resolution and stability with respect to noise in the measurements analyzed. It also shows that high-order polarization tensors provide an accurate representation of the target. Moreover, a dictionary-matching technique based on new invariants for the generalized ...
Robust statistical approaches to assess the degree of agreement of clinical data
Grilo, Luís M.; Grilo, Helena L.
2016-06-01
To analyze the blood of patients who took vitamin B12 for a period of time, two different medicine measurement methods were used (one is the established method, with more human intervention, and the other method uses essentially machines). Given the non-normality of the differences between both measurement methods, the limits of agreement are estimated using also a non-parametric approach to assess the degree of agreement of the clinical data. The bootstrap resampling method is applied in order to obtain robust confidence intervals for mean and median of differences. The approaches used are easy to apply, running a friendly software, and their outputs are also easy to interpret. In this case study the results obtained with (non)parametric approaches lead us to different statistical conclusions, but the decision whether agreement is acceptable or not is always a clinical judgment.
Statistical methods for categorical data analysis
Powers, Daniel
2008-01-01
This book provides a comprehensive introduction to methods and models for categorical data analysis and their applications in social science research. Companion website also available, at https://webspace.utexas.edu/dpowers/www/
Simple statistical methods for software engineering data and patterns
Pandian, C Ravindranath
2015-01-01
Although there are countless books on statistics, few are dedicated to the application of statistical methods to software engineering. Simple Statistical Methods for Software Engineering: Data and Patterns fills that void. Instead of delving into overly complex statistics, the book details simpler solutions that are just as effective and connect with the intuition of problem solvers.Sharing valuable insights into software engineering problems and solutions, the book not only explains the required statistical methods, but also provides many examples, review questions, and case studies that prov
Statistical methods and computing for big data
Wang, Chun; Chen, Ming-Hui; Schifano, Elizabeth; Wu, Jing
2016-01-01
Big data are data on a massive scale in terms of volume, intensity, and complexity that exceed the capacity of standard analytic tools. They present opportunities as well as challenges to statisticians. The role of computational statisticians in scientific discovery from big data analyses has been under-recognized even by peer statisticians. This article summarizes recent methodological and software developments in statistics that address the big data challenges. Methodologies are grouped into three classes: subsampling-based, divide and conquer, and online updating for stream data. As a new contribution, the online updating approach is extended to variable selection with commonly used criteria, and their performances are assessed in a simulation study with stream data. Software packages are summarized with focuses on the open source R and R packages, covering recent tools that help break the barriers of computer memory and computing power. Some of the tools are illustrated in a case study with a logistic regression for the chance of airline delay. PMID:27695593
Wang, Ying; Feng, Chenglian; Liu, Yuedan; Zhao, Yujie; Li, Huixian; Zhao, Tianhui; Guo, Wenjing
2017-02-01
Transition metals in the fourth period of the periodic table of the elements are widely widespread in aquatic environments. They could often occur at certain concentrations to cause adverse effects on aquatic life and human health. Generally, parametric models are mostly used to construct species sensitivity distributions (SSDs), which result in comparison for water quality criteria (WQC) of elements in the same period or group of the periodic table might be inaccurate and the results could be biased. To address this inadequacy, the non-parametric kernel density estimation (NPKDE) with its optimal bandwidths and testing methods were developed for establishing SSDs. The NPKDE was better fit, more robustness and better predicted than conventional normal and logistic parametric density estimations for constructing SSDs and deriving acute HC5 and WQC for transition metals in the fourth period of the periodic table. The decreasing sequence of HC5 values for the transition metals in the fourth period was Ti > Mn > V > Ni > Zn > Cu > Fe > Co > Cr(VI), which were not proportional to atomic number in the periodic table, and for different metals the relatively sensitive species were also different. The results indicated that except for physical and chemical properties there are other factors affecting toxicity mechanisms of transition metals. The proposed method enriched the methodological foundation for WQC. Meanwhile, it also provided a relatively innovative, accurate approach for the WQC derivation and risk assessment of the same group and period metals in aquatic environments to support protection of aquatic organisms.
Non-parametric determination of H and He IS fluxes from cosmic-ray data
Ghelfi, A; Derome, L; Maurin, D
2015-01-01
Top-of-atmosphere (TOA) cosmic-ray (CR) fluxes from satellites and balloon-borne experiments are snapshots of the solar activity imprinted on the interstellar (IS) fluxes. Given a series of snapshots, the unknown IS flux shape and the level of modulation (for each snapshot) can be recovered. We wish (i) to provide the most accurate determination of the IS H and He fluxes from TOA data only, (ii) to obtain the associated modulation levels (and uncertainties) fully accounting for the correlations with the IS flux uncertainties, and (iii) to inspect whether the minimal Force-Field approximation is sufficient to explain all the data at hand. Using H and He TOA measurements, including the recent high precision AMS, BESS-Polar and PAMELA data, we perform a non-parametric fit of the IS fluxes $J^{\\rm IS}_{\\rm H,~He}$ and modulation level $\\phi_i$ for each data taking period. We rely on a Markov Chain Monte Carlo (MCMC) engine to extract the PDF and correlations (hence the credible intervals) of the sought parameters...
Galindo-Garre, Francisca; Hidalgo, María Dolores; Guilera, Georgina; Pino, Oscar; Rojo, J Emilio; Gómez-Benito, Juana
2015-03-01
The World Health Organization Disability Assessment Schedule II (WHO-DAS II) is a multidimensional instrument developed for measuring disability. It comprises six domains (getting around, self-care, getting along with others, life activities and participation in society). The main purpose of this paper is the evaluation of the psychometric properties for each domain of the WHO-DAS II with parametric and non-parametric Item Response Theory (IRT) models. A secondary objective is to assess whether the WHO-DAS II items within each domain form a hierarchy of invariantly ordered severity indicators of disability. A sample of 352 patients with a schizophrenia spectrum disorder is used in this study. The 36 items WHO-DAS II was administered during the consultation. Partial Credit and Mokken scale models are used to study the psychometric properties of the questionnaire. The psychometric properties of the WHO-DAS II scale are satisfactory for all the domains. However, we identify a few items that do not discriminate satisfactorily between different levels of disability and cannot be invariantly ordered in the scale. In conclusion the WHO-DAS II can be used to assess overall disability in patients with schizophrenia, but some domains are too general to assess functionality in these patients because they contain items that are not applicable to this pathology.
Decision making in coal mine planning using a non-parametric technique of indicator kriging
Energy Technology Data Exchange (ETDEWEB)
Mamurekli, D. [Hacettepe University, Ankara (Turkey). Mining Engineering Dept.
1997-03-01
In countries where low calorific value coal reserves are abundant and oil reserves are short or none, the requirement of energy production is mainly supported by coal-fired power stations. Consequently, planning to mine the low calorific value coal deposits gains much importance considering the technical and environmental restrictions. Such a mine in Kangal Town of Sivas City is the one that delivers run of mine coal directly to the power station built in the region. In case the calorific value and the ash content of the extracted coal are lower and higher than the required limits, 1300 kcal/kg and 21%, respectively, the power station may apply penalties to the coal producing company. Since the delivery is continuous and made by relying on in situ determination of pre-estimated values these assessments without defining any confidence levels are inevitably subject to inaccuracy. Thus, the company should be aware of uncertainties in making decisions and avoid conceivable risks. In this study, valuable information is provided in the form of conditional distribution to be used during planning process. It maps the indicator variogram corresponding to calorific value of 1300 kcal/kg and the ash content of 21% estimating the conditional probabilities that the true ash contents are less and calorific values are higher than the critical limits by the application of non-parametric technique, indicator kriging. In addition, it outlines the areas that are most uncertain for decision making. 4 refs., 8 figs., 3 tabs.
Non-parametric Deprojection of Surface Brightness Profiles of Galaxies in Generalised Geometries
Chakrabarty, Dalia
2009-01-01
We present a new Bayesian non-parametric deprojection algorithm DOPING (Deprojection of Observed Photometry using and INverse Gambit), that is designed to extract 3-D luminosity density distributions $\\rho$ from observed surface brightness maps $I$, in generalised geometries, while taking into account changes in intrinsic shape with radius, using a penalised likelihood approach and an MCMC optimiser. We provide the most likely solution to the integral equation that represents deprojection of the measured $I$ to $\\rho$. In order to keep the solution modular, we choose to express $\\rho$ as a function of the line-of-sight (LOS) coordinate $z$. We calculate the extent of the system along the ${\\bf z}$-axis, for a given point on the image that lies within an identified isophotal annulus. The extent along the LOS is binned and density is held a constant over each such $z$-bin. The code begins with a seed density and at the beginning of an iterative step, the trial $\\rho$ is updated. Comparison of the projection of ...
Spectral decompositions of multiple time series: a Bayesian non-parametric approach.
Macaro, Christian; Prado, Raquel
2014-01-01
We consider spectral decompositions of multiple time series that arise in studies where the interest lies in assessing the influence of two or more factors. We write the spectral density of each time series as a sum of the spectral densities associated to the different levels of the factors. We then use Whittle's approximation to the likelihood function and follow a Bayesian non-parametric approach to obtain posterior inference on the spectral densities based on Bernstein-Dirichlet prior distributions. The prior is strategically important as it carries identifiability conditions for the models and allows us to quantify our degree of confidence in such conditions. A Markov chain Monte Carlo (MCMC) algorithm for posterior inference within this class of frequency-domain models is presented.We illustrate the approach by analyzing simulated and real data via spectral one-way and two-way models. In particular, we present an analysis of functional magnetic resonance imaging (fMRI) brain responses measured in individuals who participated in a designed experiment to study pain perception in humans.
A Non-parametric Approach to the Overall Estimate of Cognitive Load Using NIRS Time Series.
Keshmiri, Soheil; Sumioka, Hidenobu; Yamazaki, Ryuji; Ishiguro, Hiroshi
2017-01-01
We present a non-parametric approach to prediction of the n-back n ∈ {1, 2} task as a proxy measure of mental workload using Near Infrared Spectroscopy (NIRS) data. In particular, we focus on measuring the mental workload through hemodynamic responses in the brain induced by these tasks, thereby realizing the potential that they can offer for their detection in real world scenarios (e.g., difficulty of a conversation). Our approach takes advantage of intrinsic linearity that is inherent in the components of the NIRS time series to adopt a one-step regression strategy. We demonstrate the correctness of our approach through its mathematical analysis. Furthermore, we study the performance of our model in an inter-subject setting in contrast with state-of-the-art techniques in the literature to show a significant improvement on prediction of these tasks (82.50 and 86.40% for female and male participants, respectively). Moreover, our empirical analysis suggest a gender difference effect on the performance of the classifiers (with male data exhibiting a higher non-linearity) along with the left-lateralized activation in both genders with higher specificity in females.
Dimas, George; Iakovidis, Dimitris K.; Karargyris, Alexandros; Ciuti, Gastone; Koulaouzidis, Anastasios
2017-09-01
Wireless capsule endoscopy is a non-invasive screening procedure of the gastrointestinal (GI) tract performed with an ingestible capsule endoscope (CE) of the size of a large vitamin pill. Such endoscopes are equipped with a usually low-frame-rate color camera which enables the visualization of the GI lumen and the detection of pathologies. The localization of the commercially available CEs is performed in the 3D abdominal space using radio-frequency (RF) triangulation from external sensor arrays, in combination with transit time estimation. State-of-the-art approaches, such as magnetic localization, which have been experimentally proved more accurate than the RF approach, are still at an early stage. Recently, we have demonstrated that CE localization is feasible using solely visual cues and geometric models. However, such approaches depend on camera parameters, many of which are unknown. In this paper the authors propose a novel non-parametric visual odometry (VO) approach to CE localization based on a feed-forward neural network architecture. The effectiveness of this approach in comparison to state-of-the-art geometric VO approaches is validated using a robotic-assisted in vitro experimental setup.
Non-parametric mass reconstruction of A1689 from strong lensing data with SLAP
Diego-Rodriguez, J M; Protopapas, P; Tegmark, M; Benítez, N; Broadhurst, T J
2004-01-01
We present the mass distribution in the central area of the cluster A1689 by fitting over 100 multiply lensed images with the non-parametric Strong Lensing Analysis Package (SLAP, Diego et al. 2004). The surface mass distribution is obtained in a robust way finding a total mass of 0.25E15 M_sun/h within a 70'' circle radius from the central peak. Our reconstructed density profile fits well an NFW profile with small perturbations due to substructure and is compatible with the more model dependent analysis of Broadhurst et al. (2004a) based on the same data. Our estimated mass does not rely on any prior information about the distribution of dark matter in the cluster. The peak of the mass distribution falls very close to the central cD and there is substructure near the center suggesting that the cluster is not fully relaxed. We also examine the effect on the recovered mass when we include the uncertainties in the redshift of the sources and in the original shape of the sources. Using simulations designed to mi...
A Non-parametric Approach to Constrain the Transfer Function in Reverberation Mapping
Li, Yan-Rong; Bai, Jin-Ming
2016-01-01
Broad emission lines of active galactic nuclei stem from a spatially extended region (broad-line region; BLR) that are composed of discrete clouds and photoionized by the central ionizing continuum. The temporal behaviors of these emission lines are blurred echoes of the continuum variations (i.e., reverberation mapping; RM) and directly reflect structures and kinematics information of BLRs through the so-called transfer function (also known as velocity-delay map). Based on the previous works of Rybicki & Press (1992) and Zu et al. (2011), we develop an extended, non-parametric approach to determine the transfer function for RM data, in which the transfer function is expressed as a sum of a family of relatively-displaced Gaussian response functions. As such, arbitrary shapes of transfer functions associated with complicated BLR geometry can be seamlessly included, enabling us to relax the presumption of a specified transfer function frequently adopted in previous studies and to let it be determined by obs...
Statistical methods for analysing complex genetic traits
El Galta, Rachid
2006-01-01
Complex traits are caused by multiple genetic and environmental factors, and are therefore difficult to study compared with simple Mendelian diseases. The modes of inheritance of Mendelian diseases are often known. Methods to dissect such diseases are well described in literature. For complex geneti
Analysis of Statistical Methods Currently used in Toxicology Journals
Na, Jihye; Yang, Hyeri; Bae, SeungJin; Lim, Kyung-Min
2014-01-01
Statistical methods are frequently used in toxicology, yet it is not clear whether the methods employed by the studies are used consistently and conducted based on sound statistical grounds. The purpose of this paper is to describe statistical methods used in top toxicology journals. More specifically, we sampled 30 papers published in 2014 from Toxicology and Applied Pharmacology, Archives of Toxicology, and Toxicological Science and described methodologies used to provide descriptive and in...
Problems and Recommendations for Rural Statistics and Survey Methods
Institute of Scientific and Technical Information of China (English)
Chengjun; ZHANG
2014-01-01
With constant deepening of the reform and opening-up,national economic system has changed from planned economy to market economy,and rural survey and statistics remain in a difficult transition period. In this period,China needs transforming original statistical mode according to market economic system. All levels of government should report and submit a lot and increasing statistical information. Besides,in this period,townships,villages and counties are faced with old and new conflicts. These conflicts perplex implementation of rural statistics and survey and development of rural statistical undertaking,and also cause researches and thinking of reform of rural statistical and survey methods.
Statistical Methods Used in Gifted Education Journals, 2006-2010
Warne, Russell T.; Lazo, Maria; Ramos, Tammy; Ritter, Nicola
2012-01-01
This article describes the statistical methods used in quantitative and mixed methods articles between 2006 and 2010 in five gifted education research journals. Results indicate that the most commonly used statistical methods are means (85.9% of articles), standard deviations (77.8%), Pearson's "r" (47.8%), X[superscript 2] (32.2%), ANOVA (30.7%),…
Statistical methods for assessment of blend homogeneity
DEFF Research Database (Denmark)
Madsen, Camilla
2002-01-01
as powder blends there is no natural unit or amount to define a sample from the blend, and partly that current technology does not provide a method of universally collecting small representative samples from large static powder beds. In the thesis a number of methods to assess (in)homogeneity are presented...... of internal factors to the blend e.g. the particle size distribution. The relation between particle size distribution and the variation in drug content in blend and tablet samples is discussed. A central problem is to develop acceptance criteria for blends and tablet batches to decide whether the blend...... blend or batch. In the thesis it is shown how to link sampling result and acceptance criteria to the actual quality (homogeneity) of the blend or tablet batch. Also it is discussed how the assurance related to a specific acceptance criteria can be obtained from the corresponding OC-curve. Further...
Statistical methods for handling incomplete data
Kim, Jae Kwang
2013-01-01
""… this book nicely blends the theoretical material and its application through examples, and will be of interest to students and researchers as a textbook or a reference book. Extensive coverage of recent advances in handling missing data provides resources and guidelines for researchers and practitioners in implementing the methods in new settings. … I plan to use this as a textbook for my teaching and highly recommend it.""-Biometrics, September 2014
Predicting students’ grades using fuzzy non-parametric regression method and ReliefF-based algorithm
Directory of Open Access Journals (Sweden)
Javad Ghasemian
Full Text Available In this paper we introduce two new approaches to predict the grades that university students will acquire in the final exam of a course and improve the obtained result on some features extracted from logged data in an educational web-based system. First w ...
Martinez Manzanera, Octavio; Elting, Jan Willem; van der Hoeven, Johannes H; Maurits, Natasha M
2016-01-01
In the clinic, tremor is diagnosed during a time-limited process in which patients are observed and the characteristics of tremor are visually assessed. For some tremor disorders, a more detailed analysis of these characteristics is needed. Accelerometry and electromyography can be used to obtain a
Alternative methods of marginal abatement cost estimation: Non- parametric distance functions
Energy Technology Data Exchange (ETDEWEB)
Boyd, G.; Molburg, J. [Argonne National Lab., IL (United States). Decision and Information Sciences Div.; Prince, R. [USDOE Office of Environmental Analysis, Washington, DC (United States)
1996-12-31
This project implements a economic methodology to measure the marginal abatement costs of pollution by measuring the lost revenue implied by an incremental reduction in pollution. It utilizes observed performance, or `best practice`, of facilities to infer the marginal abatement cost. The initial stage of the project is to use data from an earlier published study on productivity trends and pollution in electric utilities to test this approach and to provide insights on its implementation to issues of cost-benefit analysis studies needed by the Department of Energy. The basis for this marginal abatement cost estimation is a relationship between the outputs and the inputs of a firm or plant. Given a fixed set of input resources, including quasi-fixed inputs like plant and equipment and variable inputs like labor and fuel, a firm is able to produce a mix of outputs. This paper uses this theoretical view of the joint production process to implement a methodology and obtain empirical estimates of marginal abatement costs. These estimates are compared to engineering estimates.
Statistical Method of Estimating Nigerian Hydrocarbon Reserves
Directory of Open Access Journals (Sweden)
Jeffrey O. Oseh
2015-01-01
Full Text Available Hydrocarbon reserves are basic to planning and investment decisions in Petroleum Industry. Therefore its proper estimation is of considerable importance in oil and gas production. The estimation of hydrocarbon reserves in the Niger Delta Region of Nigeria has been very popular, and very successful, in the Nigerian oil and gas industry for the past 50 years. In order to fully estimate the hydrocarbon potentials in Nigerian Niger Delta Region, a clear understanding of the reserve geology and production history should be acknowledged. Reserves estimation of most fields is often performed through Material Balance and Volumetric methods. Alternatively a simple Estimation Model and Least Squares Regression may be useful or appropriate. This model is based on extrapolation of additional reserve due to exploratory drilling trend and the additional reserve factor which is due to revision of the existing fields. This Estimation model used alongside with Linear Regression Analysis in this study gives improved estimates of the fields considered, hence can be used in other Nigerian Fields with recent production history
Review of robust multivariate statistical methods in high dimension.
Filzmoser, Peter; Todorov, Valentin
2011-10-31
General ideas of robust statistics, and specifically robust statistical methods for calibration and dimension reduction are discussed. The emphasis is on analyzing high-dimensional data. The discussed methods are applied using the packages chemometrics and rrcov of the statistical software environment R. It is demonstrated how the functions can be applied to real high-dimensional data from chemometrics, and how the results can be interpreted.
Scientific Method, Statistical Method and the Speed of Light
MacKay, R. J.; Oldford, R.W.
2000-01-01
What is “statistical method”? Is it the same as “scientific method”? This paper answers the first question by specifying the elements and procedures common to all statistical investigations and organizing these into a single structure. This structure is illustrated by careful examination of the first scientific study on the speed of light carried out by A. A. Michelson in 1879. Our answer to the second question is negative. To understand this a history on the speed of light ...
An Overview of Short-term Statistical Forecasting Methods
DEFF Research Database (Denmark)
Elias, Russell J.; Montgomery, Douglas C.; Kulahci, Murat
2006-01-01
An overview of statistical forecasting methodology is given, focusing on techniques appropriate to short- and medium-term forecasts. Topics include basic definitions and terminology, smoothing methods, ARIMA models, regression methods, dynamic regression models, and transfer functions. Techniques...
An Overview of Short-term Statistical Forecasting Methods
DEFF Research Database (Denmark)
Elias, Russell J.; Montgomery, Douglas C.; Kulahci, Murat
2006-01-01
An overview of statistical forecasting methodology is given, focusing on techniques appropriate to short- and medium-term forecasts. Topics include basic definitions and terminology, smoothing methods, ARIMA models, regression methods, dynamic regression models, and transfer functions. Techniques...
Online Statistics Labs in MSW Research Methods Courses: Reducing Reluctance toward Statistics
Elliott, William; Choi, Eunhee; Friedline, Terri
2013-01-01
This article presents results from an evaluation of an online statistics lab as part of a foundations research methods course for master's-level social work students. The article discusses factors that contribute to an environment in social work that fosters attitudes of reluctance toward learning and teaching statistics in research methods…
Online Statistics Labs in MSW Research Methods Courses: Reducing Reluctance toward Statistics
Elliott, William; Choi, Eunhee; Friedline, Terri
2013-01-01
This article presents results from an evaluation of an online statistics lab as part of a foundations research methods course for master's-level social work students. The article discusses factors that contribute to an environment in social work that fosters attitudes of reluctance toward learning and teaching statistics in research methods…
Fernández-Llamazares, Alvaro; Belmonte, Jordina; Delgado, Rosario; De Linares, Concepción
2014-04-01
Airborne pollen records are a suitable indicator for the study of climate change. The present work focuses on the role of annual pollen indices for the detection of bioclimatic trends through the analysis of the aerobiological spectra of 11 taxa of great biogeographical relevance in Catalonia over an 18-year period (1994-2011), by means of different parametric and non-parametric statistical methods. Among others, two non-parametric rank-based statistical tests were performed for detecting monotonic trends in time series data of the selected airborne pollen types and we have observed that they have similar power in detecting trends. Except for those cases in which the pollen data can be well-modeled by a normal distribution, it is better to apply non-parametric statistical methods to aerobiological studies. Our results provide a reliable representation of the pollen trends in the region and suggest that greater pollen quantities are being liberated to the atmosphere in the last years, specially by Mediterranean taxa such as Pinus, Total Quercus and Evergreen Quercus, although the trends may differ geographically. Longer aerobiological monitoring periods are required to corroborate these results and survey the increasing levels of certain pollen types that could exert an impact in terms of public health.
Hassani, Hossein; Huang, Xu; Gupta, Rangan; Ghodsi, Mansi
2016-10-01
In a recent paper, Gupta et al., (2015), analyzed whether sunspot numbers cause global temperatures based on monthly data covering the period 1880:1-2013:9. The authors find that standard time domain Granger causality test fails to reject the null hypothesis that sunspot numbers do not cause global temperatures for both full and sub-samples, namely 1880:1-1936:2, 1936:3-1986:11 and 1986:12-2013:9 (identified based on tests of structural breaks). However, frequency domain causality test detects predictability for the full-sample at short (2-2.6 months) cycle lengths, but not the sub-samples. But since, full-sample causality cannot be relied upon due to structural breaks, Gupta et al., (2015) conclude that the evidence of causality running from sunspot numbers to global temperatures is weak and inconclusive. Given the importance of the issue of global warming, our current paper aims to revisit this issue of whether sunspot numbers cause global temperatures, using the same data set and sub-samples used by Gupta et al., (2015), based on an nonparametric Singular Spectrum Analysis (SSA)-based causality test. Based on this test, we however, show that sunspot numbers have predictive ability for global temperatures for the three sub-samples, over and above the full-sample. Thus, generally speaking, our non-parametric SSA-based causality test outperformed both time domain and frequency domain causality tests and highlighted that sunspot numbers have always been important in predicting global temperatures.
Bayesian Semi- and Non-Parametric Models for Longitudinal Data with Multiple Membership Effects in R
Directory of Open Access Journals (Sweden)
Terrance Savitsky
2014-03-01
Full Text Available We introduce growcurves for R that performs analysis of repeated measures multiple membership (MM data. This data structure arises in studies under which an intervention is delivered to each subject through the subjects participation in a set of multiple elements that characterize the intervention. In our motivating study design under which subjects receive a group cognitive behavioral therapy (CBT treatment, an element is a group CBT session and each subject attends multiple sessions that, together, comprise the treatment. The sets of elements, or group CBT sessions, attended by subjects will partly overlap with some of those from other subjects to induce a dependence in their responses. The growcurves package offers two alternative sets of hierarchical models: 1. Separate terms are specified for multivariate subject and MM element random effects, where the subject effects are modeled under a Dirichlet process prior to produce a semi-parametric construction; 2. A single term is employed to model joint subject-by-MM effects. A fully non-parametric dependent Dirichlet process formulation allows exploration of differences in subject responses across different MM elements. This model allows for borrowing information among subjects who express similar longitudinal trajectories for flexible estimation. growcurves deploys estimation functions to perform posterior sampling under a suite of prior options. An accompanying set of plot functions allows the user to readily extract by-subject growth curves. The design approach intends to anticipate inferential goals with tools that fully extract information from repeated measures data. Computational efficiency is achieved by performing the sampling for estimation functions using compiled C++ code.
Population pharmacokinetics of nevirapine in Malaysian HIV patients: a non-parametric approach.
Mustafa, Suzana; Yusuf, Wan Nazirah Wan; Woillard, Jean Baptiste; Choon, Tan Soo; Hassan, Norul Badriah
2016-07-01
Nevirapine is the first non-nucleoside reverse-transcriptase inhibitor approved and is widely used in combination therapy to treat HIV-1 infection. The pharmacokinetics of nevirapine was extensively studied in various populations with a parametric approach. Hence, this study was aimed to determine population pharmacokinetic parameters in Malaysian HIV-infected patients with a non-parametric approach which allows detection of outliers or non-normal distribution contrary to the parametric approach. Nevirapine population pharmacokinetics was modelled with Pmetrics. A total of 708 observations from 112 patients were included in the model building and validation analysis. Evaluation of the model was based on a visual inspection of observed versus predicted (population and individual) concentrations and plots weighted residual error versus concentrations. Accuracy and robustness of the model were evaluated by visual predictive check (VPC). The median parameters' estimates obtained from the final model were used to predict individual nevirapine plasma area-under-curve (AUC) in the validation dataset. The Bland-Altman plot was used to compare the AUC predicted with trapezoidal AUC. The median nevirapine clearance was of 2.92 L/h, the median rate of absorption was 2.55/h and the volume of distribution was 78.23 L. Nevirapine pharmacokinetics were best described by one-compartmental with first-order absorption model and a lag-time. Weighted residuals for the model selected were homogenously distributed over the concentration and time range. The developed model adequately estimated AUC. In conclusion, a model to describe the pharmacokinetics of nevirapine was developed. The developed model adequately describes nevirapine population pharmacokinetics in HIV-infected patients in Malaysia.
A Non-Parametric Item Response Theory Evaluation of the CAGE Instrument Among Older Adults.
Abdin, Edimansyah; Sagayadevan, Vathsala; Vaingankar, Janhavi Ajit; Picco, Louisa; Chong, Siow Ann; Subramaniam, Mythily
2017-08-04
The validity of the CAGE using item response theory (IRT) has not yet been examined in older adult population. This study aims to investigate the psychometric properties of the CAGE using both non-parametric and parametric IRT models, assess whether there is any differential item functioning (DIF) by age, gender and ethnicity and examine the measurement precision at the cut-off scores. We used data from the Well-being of the Singapore Elderly study to conduct Mokken scaling analysis (MSA), dichotomous Rasch and 2-parameter logistic IRT models. The measurement precision at the cut-off scores were evaluated using classification accuracy (CA) and classification consistency (CC). The MSA showed the overall scalability H index was 0.459, indicating a medium performing instrument. All items were found to be homogenous, measuring the same construct and able to discriminate well between respondents with high levels of the construct and the ones with lower levels. The item discrimination ranged from 1.07 to 6.73 while the item difficulty ranged from 0.33 to 2.80. Significant DIF was found for 2-item across ethnic group. More than 90% (CC and CA ranged from 92.5% to 94.3%) of the respondents were consistently and accurately classified by the CAGE cut-off scores of 2 and 3. The current study provides new evidence on the validity of the CAGE from the IRT perspective. This study provides valuable information of each item in the assessment of the overall severity of alcohol problem and the precision of the cut-off scores in older adult population.
The use of Statistical Methods in Mechanical Engineering
Directory of Open Access Journals (Sweden)
Iram Saleem
2013-03-01
Full Text Available Statistics is an important tool to handle the vast data of present era as statistics can interpret all the information in such a beauty that so many conclusions can be extracted from it. The aim of this study is to see the use of statistical methods in Mechanical Engineering (ME therefore; we selected research papers published in 2010 from the well reputed journals in ME under Taylor and Francis Company LTD. More than 350 research papers were downloaded from well reputed ME journals such as Inverse Problem in Science and Engineering (IPSE, Machining Science and Technology (MST, Materials and Manufacturing Processes (MMP, Particulate Science and Technology (PST and Research in Nondestructive Evaluation (RNE. We recorded the statistical techniques/methods used in each research paper. In this study, we presented frequency distribution of descriptive statistics and advance level statistical methods used in five of the ME journals in 2010.
Ocampo-Duque, William; Osorio, Carolina; Piamba, Christian; Schuhmacher, Marta; Domingo, José L
2013-02-01
The integration of water quality monitoring variables is essential in environmental decision making. Nowadays, advanced techniques to manage subjectivity, imprecision, uncertainty, vagueness, and variability are required in such complex evaluation process. We here propose a probabilistic fuzzy hybrid model to assess river water quality. Fuzzy logic reasoning has been used to compute a water quality integrative index. By applying a Monte Carlo technique, based on non-parametric probability distributions, the randomness of model inputs was estimated. Annual histograms of nine water quality variables were built with monitoring data systematically collected in the Colombian Cauca River, and probability density estimations using the kernel smoothing method were applied to fit data. Several years were assessed, and river sectors upstream and downstream the city of Santiago de Cali, a big city with basic wastewater treatment and high industrial activity, were analyzed. The probabilistic fuzzy water quality index was able to explain the reduction in water quality, as the river receives a larger number of agriculture, domestic, and industrial effluents. The results of the hybrid model were compared to traditional water quality indexes. The main advantage of the proposed method is that it considers flexible boundaries between the linguistic qualifiers used to define the water status, being the belongingness of water quality to the diverse output fuzzy sets or classes provided with percentiles and histograms, which allows classify better the real water condition. The results of this study show that fuzzy inference systems integrated to stochastic non-parametric techniques may be used as complementary tools in water quality indexing methodologies.
Kalicka, Renata; Pietrenko-Dabrowska, Anna
2007-03-01
In the paper MRI measurements are used for assessment of brain tissue perfusion and other features and functions of the brain (cerebral blood flow - CBF, cerebral blood volume - CBV, mean transit time - MTT). Perfusion is an important indicator of tissue viability and functioning as in pathological tissue blood flow, vascular and tissue structure are altered with respect to normal tissue. MRI enables diagnosing diseases at an early stage of their course. The parametric and non-parametric approaches to the identification of MRI models are presented and compared. The non-parametric modeling adopts gamma variate functions. The parametric three-compartmental catenary model, based on the general kinetic model, is also proposed. The parameters of the models are estimated on the basis of experimental data. The goodness of fit of the gamma variate and the three-compartmental models to the data and the accuracy of the parameter estimates are compared. Kalman filtering, smoothing the measurements, was adopted to improve the estimate accuracy of the parametric model. Parametric modeling gives a better fit and better parameter estimates than non-parametric and allows an insight into the functioning of the system. To improve the accuracy optimal experiment design related to the input signal was performed.
A new non-parametric stationarity test of time series in the time domain
Jin, Lei
2014-11-07
© 2015 The Royal Statistical Society and Blackwell Publishing Ltd. We propose a new double-order selection test for checking second-order stationarity of a time series. To develop the test, a sequence of systematic samples is defined via Walsh functions. Then the deviations of the autocovariances based on these systematic samples from the corresponding autocovariances of the whole time series are calculated and the uniform asymptotic joint normality of these deviations over different systematic samples is obtained. With a double-order selection scheme, our test statistic is constructed by combining the deviations at different lags in the systematic samples. The null asymptotic distribution of the statistic proposed is derived and the consistency of the test is shown under fixed and local alternatives. Simulation studies demonstrate well-behaved finite sample properties of the method proposed. Comparisons with some existing tests in terms of power are given both analytically and empirically. In addition, the method proposed is applied to check the stationarity assumption of a chemical process viscosity readings data set.
Statistical methods in longitudinal research principles and structuring change
von Eye, Alexander
1991-01-01
These edited volumes present new statistical methods in a way that bridges the gap between theoretical and applied statistics. The volumes cover general problems and issues and more specific topics concerning the structuring of change, the analysis of time series, and the analysis of categorical longitudinal data. The book targets students of development and change in a variety of fields - psychology, sociology, anthropology, education, medicine, psychiatry, economics, behavioural sciences, developmental psychology, ecology, plant physiology, and biometry - with basic training in statistics an
Crosta, Fernando; Nishiwaki-Dantas, Maria Cristina; Silvino, Wilmar; Dantas, Paulo Elias Correa
2005-01-01
To verify the frequency of study design, applied statistical analysis and approval by institutional review offices (Ethics Committee) of articles published in the "Arquivos Brasileiros de Oftalmologia" during a 10-year interval, with later comparative and critical analysis by some of the main international journals in the field of Ophthalmology. Systematic review without metanalysis was performed. Scientific papers published in the "Arquivos Brasileiros de Oftalmologia" between January 1993 and December 2002 were reviewed by two independent reviewers and classified according to the applied study design, statistical analysis and approval by the institutional review offices. To categorize those variables, a descriptive statistical analysis was used. After applying inclusion and exclusion criteria, 584 articles for evaluation of statistical analysis and, 725 articles for evaluation of study design were reviewed. Contingency table (23.10%) was the most frequently applied statistical method, followed by non-parametric tests (18.19%), Student's t test (12.65%), central tendency measures (10.60%) and analysis of variance (9.81%). Of 584 reviewed articles, 291 (49.82%) presented no statistical analysis. Observational case series (26.48%) was the most frequently used type of study design, followed by interventional case series (18.48%), observational case description (13.37%), non-random clinical study (8.96%) and experimental study (8.55%). We found a higher frequency of observational clinical studies, lack of statistical analysis in almost half of the published papers. Increase in studies with approval by institutional review Ethics Committee was noted since it became mandatory in 1996.
The estimation of the measurement results with using statistical methods
Velychko, O.; Gordiyenko, T.
2015-02-01
The row of international standards and guides describe various statistical methods that apply for a management, control and improvement of processes with the purpose of realization of analysis of the technical measurement results. The analysis of international standards and guides on statistical methods estimation of the measurement results recommendations for those applications in laboratories is described. For realization of analysis of standards and guides the cause-and-effect Ishikawa diagrams concerting to application of statistical methods for estimation of the measurement results are constructed.
Non-parametric probabilistic forecasts of wind power: required properties and evaluation
DEFF Research Database (Denmark)
Pinson, Pierre; Nielsen, Henrik Aalborg; Møller, Jan Kloppenborg;
2007-01-01
of the conditional expectation of future generation for each look-ahead time, but also with uncertainty estimates given by probabilistic forecasts. In order to avoid assumptions on the shape of predictive distributions, these probabilistic predictions are produced from nonparametric methods, and then take the form...... of a single or a set of quantile forecasts. The required and desirable properties of such probabilistic forecasts are defined and a framework for their evaluation is proposed. This framework is applied for evaluating the quality of two statistical methods producing full predictive distributions from point......Predictions of wind power production for horizons up to 48-72 hour ahead comprise a highly valuable input to the methods for the daily management or trading of wind generation. Today, users of wind power predictions are not only provided with point predictions, which are estimates...
Evaluation of world's largest social welfare scheme: An assessment using non-parametric approach.
Singh, Sanjeet
2016-08-01
Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) is the world's largest social welfare scheme in India for the poverty alleviation through rural employment generation. This paper aims to evaluate and rank the performance of the states in India under MGNREGA scheme. A non-parametric approach, Data Envelopment Analysis (DEA) is used to calculate the overall technical, pure technical, and scale efficiencies of states in India. The sample data is drawn from the annual official reports published by the Ministry of Rural Development, Government of India. Based on three selected input parameters (expenditure indicators) and five output parameters (employment generation indicators), I apply both input and output oriented DEA models to estimate how well the states utilize their resources and generate outputs during the financial year 2013-14. The relative performance evaluation has been made under the assumption of constant returns and also under variable returns to scale to assess the impact of scale on performance. The results indicate that the main source of inefficiency is both technical and managerial practices adopted. 11 states are overall technically efficient and operate at the optimum scale whereas 18 states are pure technical or managerially efficient. It has been found that for some states it necessary to alter scheme size to perform at par with the best performing states. For inefficient states optimal input and output targets along with the resource savings and output gains are calculated. Analysis shows that if all inefficient states operate at optimal input and output levels, on an average 17.89% of total expenditure and a total amount of $780million could have been saved in a single year. Most of the inefficient states perform poorly when it comes to the participation of women and disadvantaged sections (SC&ST) in the scheme. In order to catch up with the performance of best performing states, inefficient states on an average need to enhance
Grade-Average Method: A Statistical Approach for Estimating ...
African Journals Online (AJOL)
Grade-Average Method: A Statistical Approach for Estimating Missing Value for Continuous Assessment Marks. ... Journal of the Nigerian Association of Mathematical Physics. Journal Home · ABOUT ... Open Access DOWNLOAD FULL TEXT ...
Methods of quantum field theory in statistical physics
Abrikosov, A A; Gorkov, L P; Silverman, Richard A
1975-01-01
This comprehensive introduction to the many-body theory was written by three renowned physicists and acclaimed by American Scientist as ""a classic text on field theoretic methods in statistical physics."
Steganalytic method based on short and repeated sequence distance statistics
Institute of Scientific and Technical Information of China (English)
WANG GuoXin; PING XiJian; XU ManKun; ZHANG Tao; BAO XiRui
2008-01-01
According to the distribution characteristics of short and repeated sequence (SRS),a steganalytic method based on the correlation of image bit planes is proposed.Firstly,we provide the conception of SRS distance statistics and deduce its statistical distribution.Because the SRS distance statistics can effectively reflect the correlation of the sequence,SRS has statistical features when the image bit plane sequence equals the image width.Using this characteristic,the steganalytic method is fulfilled by the distinct test of Poisson distribution.Experimental results show a good performance for detecting LSB matching steganographic method in still images.By the way,the proposed method is not designed for specific steganographic algorithms and has good generality.
Longitudinal data analysis a handbook of modern statistical methods
Fitzmaurice, Garrett; Verbeke, Geert; Molenberghs, Geert
2008-01-01
Although many books currently available describe statistical models and methods for analyzing longitudinal data, they do not highlight connections between various research threads in the statistical literature. Responding to this void, Longitudinal Data Analysis provides a clear, comprehensive, and unified overview of state-of-the-art theory and applications. It also focuses on the assorted challenges that arise in analyzing longitudinal data. After discussing historical aspects, leading researchers explore four broad themes: parametric modeling, nonparametric and semiparametric methods, joint
Complex Data Modeling and Computationally Intensive Statistical Methods
Mantovan, Pietro
2010-01-01
The last years have seen the advent and development of many devices able to record and store an always increasing amount of complex and high dimensional data; 3D images generated by medical scanners or satellite remote sensing, DNA microarrays, real time financial data, system control datasets. The analysis of this data poses new challenging problems and requires the development of novel statistical models and computational methods, fueling many fascinating and fast growing research areas of modern statistics. The book offers a wide variety of statistical methods and is addressed to statistici
Method for statistical data analysis of multivariate observations
Gnanadesikan, R
1997-01-01
A practical guide for multivariate statistical techniques-- now updated and revised In recent years, innovations in computer technology and statistical methodologies have dramatically altered the landscape of multivariate data analysis. This new edition of Methods for Statistical Data Analysis of Multivariate Observations explores current multivariate concepts and techniques while retaining the same practical focus of its predecessor. It integrates methods and data-based interpretations relevant to multivariate analysis in a way that addresses real-world problems arising in many areas of inte
Chung, Clement; Emili, Andrew; Frey, Brendan J
2013-04-01
Tandem mass spectrometry (MS/MS) is a dominant approach for large-scale high-throughput post-translational modification (PTM) profiling. Although current state-of-the-art blind PTM spectral analysis algorithms can predict thousands of modified peptides (PTM predictions) in an MS/MS experiment, a significant percentage of these predictions have inaccurate modification mass estimates and false modification site assignments. This problem can be addressed by post-processing the PTM predictions with a PTM refinement algorithm. We developed a novel PTM refinement algorithm, iPTMClust, which extends a recently introduced PTM refinement algorithm PTMClust and uses a non-parametric Bayesian model to better account for uncertainties in the quantity and identity of PTMs in the input data. The use of this new modeling approach enables iPTMClust to provide a confidence score per modification site that allows fine-tuning and interpreting resulting PTM predictions. The primary goal behind iPTMClust is to improve the quality of the PTM predictions. First, to demonstrate that iPTMClust produces sensible and accurate cluster assignments, we compare it with k-means clustering, mixtures of Gaussians (MOG) and PTMClust on a synthetically generated PTM dataset. Second, in two separate benchmark experiments using PTM data taken from a phosphopeptide and a yeast proteome study, we show that iPTMClust outperforms state-of-the-art PTM prediction and refinement algorithms, including PTMClust. Finally, we illustrate the general applicability of our new approach on a set of human chromatin protein complex data, where we are able to identify putative novel modified peptides and modification sites that may be involved in the formation and regulation of protein complexes. Our method facilitates accurate PTM profiling, which is an important step in understanding the mechanisms behind many biological processes and should be an integral part of any proteomic study. Our algorithm is implemented in
A Circular Statistical Method for Extracting Rotation Measures
Indian Academy of Sciences (India)
S. Sarala; Pankaj Jain
2002-03-01
We propose a new method for the extraction of Rotation Measures from spectral polarization data. The method is based on maximum likelihood analysis and takes into account the circular nature of the polarization data. The method is unbiased and statistically more efficient than the standard 2 procedure.
Statistical Analysis of Data for Timber Strengths
DEFF Research Database (Denmark)
Sørensen, John Dalsgaard
2003-01-01
Statistical analyses are performed for material strength parameters from a large number of specimens of structural timber. Non-parametric statistical analysis and fits have been investigated for the following distribution types: Normal, Lognormal, 2 parameter Weibull and 3-parameter Weibull....... The statistical fits have generally been made using all data and the lower tail of the data. The Maximum Likelihood Method and the Least Square Technique have been used to estimate the statistical parameters in the selected distributions. The results show that the 2-parameter Weibull distribution gives the best...... fits to the data available, especially if tail fits are used whereas the Log Normal distribution generally gives a poor fit and larger coefficients of variation, especially if tail fits are used. The implications on the reliability level of typical structural elements and on partial safety factors...
Statistical Analysis of Data for Timber Strengths
DEFF Research Database (Denmark)
Sørensen, John Dalsgaard; Hoffmeyer, P.
Statistical analyses are performed for material strength parameters from approximately 6700 specimens of structural timber. Non-parametric statistical analyses and fits to the following distributions types have been investigated: Normal, Lognormal, 2 parameter Weibull and 3-parameter Weibull....... The statistical fits have generally been made using all data (100%) and the lower tail (30%) of the data. The Maximum Likelihood Method and the Least Square Technique have been used to estimate the statistical parameters in the selected distributions. 8 different databases are analysed. The results show that 2......-parameter Weibull (and Normal) distributions give the best fits to the data available, especially if tail fits are used whereas the LogNormal distribution generally gives poor fit and larger coefficients of variation, especially if tail fits are used....
Statistical Methods for Single-Particle Electron Cryomicroscopy
DEFF Research Database (Denmark)
Jensen, Katrine Hommelhoff
from the noisy, randomly oriented projection images. Many statistical approaches to SPR have been proposed in the past. Typically, due to the computation time complexity, they rely on approximated maximum likelihood (ML) or maximum a posteriori (MAP) estimate of the structure. All methods presented...... between a MAP approach for estimating the protein structure. The resulting method is statistically optimal under the assumption of the uniform prior in the space of rotations. The marginal posterior is constructed by integrating over the view orientations and maximised by the expectation-maximisation (EM...... in this thesis attempt to solve a specific part of the reconstruction problem in a statistically sound manner. Firstly, we propose two methods for solving the problems (1) and (2). They can ultimately be extended and combined into a statistically sound solution to the full SPR problem. We use Bayesian...
Analysis of Statistical Methods Currently used in Toxicology Journals.
Na, Jihye; Yang, Hyeri; Bae, SeungJin; Lim, Kyung-Min
2014-09-01
Statistical methods are frequently used in toxicology, yet it is not clear whether the methods employed by the studies are used consistently and conducted based on sound statistical grounds. The purpose of this paper is to describe statistical methods used in top toxicology journals. More specifically, we sampled 30 papers published in 2014 from Toxicology and Applied Pharmacology, Archives of Toxicology, and Toxicological Science and described methodologies used to provide descriptive and inferential statistics. One hundred thirteen endpoints were observed in those 30 papers, and most studies had sample size less than 10, with the median and the mode being 6 and 3 & 6, respectively. Mean (105/113, 93%) was dominantly used to measure central tendency, and standard error of the mean (64/113, 57%) and standard deviation (39/113, 34%) were used to measure dispersion, while few studies provide justifications regarding why the methods being selected. Inferential statistics were frequently conducted (93/113, 82%), with one-way ANOVA being most popular (52/93, 56%), yet few studies conducted either normality or equal variance test. These results suggest that more consistent and appropriate use of statistical method is necessary which may enhance the role of toxicology in public health.
Oxygen Abundance Methods in SDSS: View from Modern Statistics
Indian Academy of Sciences (India)
Fei Shi; Gang Zhao; James Wicker
2010-09-01
Our purpose is to find which is the most reliable one among various oxygen abundance determination methods. We will test the validity of several different oxygen abundance determination methods using methods of modern statistics. These methods include Bayesian analysis and information scoring. We will analyze a sample of ∼ 6000 HII galaxies from the Sloan Digital Sky Survey (SDSS) spectroscopic observations data release four. All methods that we used drew the same conclusion that the method is a more reliable oxygen abundance determination method than the Bayesian metallicity method under the existing telescope ability. The ratios of the likelihoods between the different kinds of methods tell us that the , , and 32 methods are consistent with each other because the and 32 methods are calibrated by method. The Bayesian and 23 methods are consistent with each other because both are calibrated by a galaxy model. In either case, the 2 method is an unreliable method.
Brief guidelines for methods and statistics in medical research
Ab Rahman, Jamalludin
2015-01-01
This book serves as a practical guide to methods and statistics in medical research. It includes step-by-step instructions on using SPSS software for statistical analysis, as well as relevant examples to help those readers who are new to research in health and medical fields. Simple texts and diagrams are provided to help explain the concepts covered, and print screens for the statistical steps and the SPSS outputs are provided, together with interpretations and examples of how to report on findings. Brief Guidelines for Methods and Statistics in Medical Research offers a valuable quick reference guide for healthcare students and practitioners conducting research in health related fields, written in an accessible style.
Statistical Methods for Characterizing Variability in Stellar Spectra
Cisewski, Jessi; Yale Astrostatistics
2017-01-01
Recent years have seen a proliferation in the number of exoplanets discovered. One technique for uncovering exoplanets relies on the detection of subtle shifts in the stellar spectra due to the Doppler effect caused by an orbiting object. However, stellar activity can cause distortions in the spectra that mimic the imprint of an orbiting exoplanet. The collection of stellar spectra potentially contains more information than is traditionally used for estimating its radial velocity curve. I will discuss some statistical methods that can be used for characterizing the sources of variability in the spectra. Statistical assessment of stellar spectra is a focus of the Statistical and Applied Mathematical Sciences Institute (SAMSI)'s yearlong program on Statistical, Mathematical and Computational Methods for Astronomy's Working Group IV (Astrophysical Populations).
Fundamentals of modern statistical methods substantially improving power and accuracy
Wilcox, Rand R
2001-01-01
Conventional statistical methods have a very serious flaw They routinely miss differences among groups or associations among variables that are detected by more modern techniques - even under very small departures from normality Hundreds of journal articles have described the reasons standard techniques can be unsatisfactory, but simple, intuitive explanations are generally unavailable Improved methods have been derived, but they are far from obvious or intuitive based on the training most researchers receive Situations arise where even highly nonsignificant results become significant when analyzed with more modern methods Without assuming any prior training in statistics, Part I of this book describes basic statistical principles from a point of view that makes their shortcomings intuitive and easy to understand The emphasis is on verbal and graphical descriptions of concepts Part II describes modern methods that address the problems covered in Part I Using data from actual studies, many examples are include...
Complexity of software trustworthiness and its dynamical statistical analysis methods
Institute of Scientific and Technical Information of China (English)
ZHENG ZhiMing; MA ShiLong; LI Wei; JIANG Xin; WEI Wei; MA LiLi; TANG ShaoTing
2009-01-01
Developing trusted softwares has become an important trend and a natural choice in the development of software technology and applications.At present,the method of measurement and assessment of software trustworthiness cannot guarantee safe and reliable operations of software systems completely and effectively.Based on the dynamical system study,this paper interprets the characteristics of behaviors of software systems and the basic scientific problems of software trustworthiness complexity,analyzes the characteristics of complexity of software trustworthiness,and proposes to study the software trustworthiness measurement in terms of the complexity of software trustworthiness.Using the dynamical statistical analysis methods,the paper advances an invariant-measure based assessment method of software trustworthiness by statistical indices,and hereby provides a dynamical criterion for the untrustworthiness of software systems.By an example,the feasibility of the proposed dynamical statistical analysis method in software trustworthiness measurement is demonstrated using numerical simulations and theoretical analysis.
Statistical Methods for Quantitatively Detecting Fungal Disease from Fruits’ Images
Jagadeesh D. Pujari; Yakkundimath, Rajesh Siddaramayya; Byadgi, Abdulmunaf Syedhusain
2013-01-01
In this paper we have proposed statistical methods for detecting fungal disease and classifying based on disease severity levels. Most fruits diseases are caused by bacteria, fungi, virus, etc of which fungi are responsible for a large number of diseases in fruits. In this study images of fruits, affected by different fungal symptoms are collected and categorized based on disease severity. Statistical features like block wise, gray level co-occurrence matrix (GLCM), gray level runlength matr...
Hierarchical modelling for the environmental sciences statistical methods and applications
Clark, James S
2006-01-01
New statistical tools are changing the way in which scientists analyze and interpret data and models. Hierarchical Bayes and Markov Chain Monte Carlo methods for analysis provide a consistent framework for inference and prediction where information is heterogeneous and uncertain, processes are complicated, and responses depend on scale. Nowhere are these methods more promising than in the environmental sciences.
The Metropolis Monte Carlo Method in Statistical Physics
Landau, David P.
2003-11-01
A brief overview is given of some of the advances in statistical physics that have been made using the Metropolis Monte Carlo method. By complementing theory and experiment, these have increased our understanding of phase transitions and other phenomena in condensed matter systems. A brief description of a new method, commonly known as "Wang-Landau sampling," will also be presented.
Descriptive and inferential statistical methods used in burns research.
Al-Benna, Sammy; Al-Ajam, Yazan; Way, Benjamin; Steinstraesser, Lars
2010-05-01
Burns research articles utilise a variety of descriptive and inferential methods to present and analyse data. The aim of this study was to determine the descriptive methods (e.g. mean, median, SD, range, etc.) and survey the use of inferential methods (statistical tests) used in articles in the journal Burns. This study defined its population as all original articles published in the journal Burns in 2007. Letters to the editor, brief reports, reviews, and case reports were excluded. Study characteristics, use of descriptive statistics and the number and types of statistical methods employed were evaluated. Of the 51 articles analysed, 11(22%) were randomised controlled trials, 18(35%) were cohort studies, 11(22%) were case control studies and 11(22%) were case series. The study design and objectives were defined in all articles. All articles made use of continuous and descriptive data. Inferential statistics were used in 49(96%) articles. Data dispersion was calculated by standard deviation in 30(59%). Standard error of the mean was quoted in 19(37%). The statistical software product was named in 33(65%). Of the 49 articles that used inferential statistics, the tests were named in 47(96%). The 6 most common tests used (Student's t-test (53%), analysis of variance/co-variance (33%), chi(2) test (27%), Wilcoxon & Mann-Whitney tests (22%), Fisher's exact test (12%)) accounted for the majority (72%) of statistical methods employed. A specified significance level was named in 43(88%) and the exact significance levels were reported in 28(57%). Descriptive analysis and basic statistical techniques account for most of the statistical tests reported. This information should prove useful in deciding which tests should be emphasised in educating burn care professionals. These results highlight the need for burn care professionals to have a sound understanding of basic statistics, which is crucial in interpreting and reporting data. Advice should be sought from professionals
Neelakantan, S; Veng-Pedersen, P
2005-11-01
A novel numerical deconvolution method is presented that enables the estimation of drug absorption rates under time-variant disposition conditions. The method involves two components. (1) A disposition decomposition-recomposition (DDR) enabling exact changes in the unit impulse response (UIR) to be constructed based on centrally based clearance changes iteratively determined. (2) A non-parametric, end-constrained cubic spline (ECS) input response function estimated by cross-validation. The proposed DDR-ECS method compensates for disposition changes between the test and the reference administrations by using a "beta" clearance correction based on DDR analysis. The representation of the input response by the ECS method takes into consideration the complex absorption process and also ensures physiologically realistic approximations of the response. The stability of the new method to noisy data was evaluated by comprehensive simulations that considered different UIRs, various input functions, clearance changes and a novel scaling of the input function that includes the "flip-flop" absorption phenomena. The simulated input response was also analysed by two other methods and all three methods were compared for their relative performances. The DDR-ECS method provides better estimation of the input profile under significant clearance changes but tends to overestimate the input when there were only small changes in the clearance.
污染线性模型的非参数估计%NON-PARAMETRIC ESTIMATION IN CONTAMINATED LINEAR MODEL
Institute of Scientific and Technical Information of China (English)
柴根象; 孙燕; 杨筱菡
2001-01-01
In this paper, the following contaminated linear model is considered: yi=(1-ε)xτiβ+zi, 1≤i≤n, where r.v.'s ｛yi｝ are contaminated with errors ｛zi｝. To assume that the errors have the finite moment of order 2 only. The non-parametric estimation of contaminated coefficient ε and regression parameter β are established, and the strong consistency and convergence rate almost surely of the estimators are obtained. A simulated example is also given to show the visual performance of the estimations.
Academic Training Lecture: Statistical Methods for Particle Physics
PH Department
2012-01-01
2, 3, 4 and 5 April 2012 Academic Training Lecture Regular Programme from 11:00 to 12:00 - Bldg. 222-R-001 - Filtration Plant Statistical Methods for Particle Physics by Glen Cowan (Royal Holloway) The series of four lectures will introduce some of the important statistical methods used in Particle Physics, and should be particularly relevant to those involved in the analysis of LHC data. The lectures will include an introduction to statistical tests, parameter estimation, and the application of these tools to searches for new phenomena. Both frequentist and Bayesian methods will be described, with particular emphasis on treatment of systematic uncertainties. The lectures will also cover unfolding, that is, estimation of a distribution in binned form where the variable in question is subject to measurement errors.
Three Methods for Occupation Coding Based on Statistical Learning
Directory of Open Access Journals (Sweden)
Gweon Hyukjun
2017-03-01
Full Text Available Occupation coding, an important task in official statistics, refers to coding a respondent’s text answer into one of many hundreds of occupation codes. To date, occupation coding is still at least partially conducted manually, at great expense. We propose three methods for automatic coding: combining separate models for the detailed occupation codes and for aggregate occupation codes, a hybrid method that combines a duplicate-based approach with a statistical learning algorithm, and a modified nearest neighbor approach. Using data from the German General Social Survey (ALLBUS, we show that the proposed methods improve on both the coding accuracy of the underlying statistical learning algorithm and the coding accuracy of duplicates where duplicates exist. Further, we find defining duplicates based on ngram variables (a concept from text mining is preferable to one based on exact string matches.
Statistical Methods for Particle Physics (4/4)
CERN. Geneva
2012-01-01
The series of four lectures will introduce some of the important statistical methods used in Particle Physics, and should be particularly relevant to those involved in the analysis of LHC data. The lectures will include an introduction to statistical tests, parameter estimation, and the application of these tools to searches for new phenomena. Both frequentist and Bayesian methods will be described, with particular emphasis on treatment of systematic uncertainties. The lectures will also cover unfolding, that is, estimation of a distribution in binned form where the variable in question is subject to measurement errors.
Statistical Methods for Particle Physics (2/4)
CERN. Geneva
2012-01-01
The series of four lectures will introduce some of the important statistical methods used in Particle Physics, and should be particularly relevant to those involved in the analysis of LHC data. The lectures will include an introduction to statistical tests, parameter estimation, and the application of these tools to searches for new phenomena. Both frequentist and Bayesian methods will be described, with particular emphasis on treatment of systematic uncertainties. The lectures will also cover unfolding, that is, estimation of a distribution in binned form where the variable in question is subject to measurement errors.
Statistical Methods for Particle Physics (1/4)
CERN. Geneva
2012-01-01
The series of four lectures will introduce some of the important statistical methods used in Particle Physics, and should be particularly relevant to those involved in the analysis of LHC data. The lectures will include an introduction to statistical tests, parameter estimation, and the application of these tools to searches for new phenomena. Both frequentist and Bayesian methods will be described, with particular emphasis on treatment of systematic uncertainties. The lectures will also cover unfolding, that is, estimation of a distribution in binned form where the variable in question is subject to measurement errors.
Statistical Methods for Particle Physics (3/4)
CERN. Geneva
2012-01-01
The series of four lectures will introduce some of the important statistical methods used in Particle Physics, and should be particularly relevant to those involved in the analysis of LHC data. The lectures will include an introduction to statistical tests, parameter estimation, and the application of these tools to searches for new phenomena. Both frequentist and Bayesian methods will be described, with particular emphasis on treatment of systematic uncertainties. The lectures will also cover unfolding, that is, estimation of a distribution in binned form where the variable in question is subject to measurement errors.
Skinner, Carl G; Patel, Manish M; Thomas, Jerry D; Miller, Michael A
2011-01-01
Statistical methods are pervasive in medical research and general medical literature. Understanding general statistical concepts will enhance our ability to critically appraise the current literature and ultimately improve the delivery of patient care. This article intends to provide an overview of the common statistical methods relevant to medicine.
Ruppin, F; Comis, B; Ade, P; André, P; Arnaud, M; Beelen, A; Benoît, A; Bideaud, A; Billot, N; Bourrion, O; Calvo, M; Catalano, A; Coiffard, G; D'Addabbo, A; De Petris, M; Désert, F -X; Doyle, S; Goupy, J; Kramer, C; Leclercq, S; Macías-Pérez, J F; Mauskopf, P; Mayet, F; Monfardini, A; Pajot, F; Pascale, E; Perotto, L; Pisano, G; Pointecouteau, E; Ponthieu, N; Pratt, G W; Revéret, V; Ritacco, A; Rodriguez, L; Romero, C; Schuster, K; Sievers, A; Triqueneaux, S; Tucker, C; Zylka, R
2016-01-01
The determination of the thermodynamic properties of clusters of galaxies at intermediate and high redshift can bring new insights into the formation of large scale structures. It is essential for a robust calibration of the mass-observable scaling relations and their scatter, which are key ingredients for precise cosmology using cluster statistics. Here we illustrate an application of high-resolution $(< 20$ arcsec) thermal Sunyaev-Zel'dovich (tSZ) observations by probing the intracluster medium (ICM) of the Planck-discovered galaxy cluster PSZ1 G045.85+57.71 at redshift $z = 0.61$, using tSZ data obtained with the NIKA camera, a dual-band (150 and 260~GHz) instrument operated at the IRAM 30-meter telescope. We deproject jointly NIKA and Planck data to extract the electronic pressure distribution non-parametrically from the cluster core ($R \\sim 0.02\\, R_{500}$) to its outskirts ($R \\sim 3\\, R_{500}$), for the first time at intermediate redshift. The constraints on the resulting pressure profile allow us ...
Szöcs, Eduard; Schäfer, Ralf B
2015-09-01
Ecotoxicologists often encounter count and proportion data that are rarely normally distributed. To meet the assumptions of the linear model, such data are usually transformed or non-parametric methods are used if the transformed data still violate the assumptions. Generalized linear models (GLMs) allow to directly model such data, without the need for transformation. Here, we compare the performance of two parametric methods, i.e., (1) the linear model (assuming normality of transformed data), (2) GLMs (assuming a Poisson, negative binomial, or binomially distributed response), and (3) non-parametric methods. We simulated typical data mimicking low replicated ecotoxicological experiments of two common data types (counts and proportions from counts). We compared the performance of the different methods in terms of statistical power and Type I error for detecting a general treatment effect and determining the lowest observed effect concentration (LOEC). In addition, we outlined differences on a real-world mesocosm data set. For count data, we found that the quasi-Poisson model yielded the highest power. The negative binomial GLM resulted in increased Type I errors, which could be fixed using the parametric bootstrap. For proportions, binomial GLMs performed better than the linear model, except to determine LOEC at extremely low sample sizes. The compared non-parametric methods had generally lower power. We recommend that counts in one-factorial experiments should be analyzed using quasi-Poisson models and proportions from counts by binomial GLMs. These methods should become standard in ecotoxicology.
'nparACT' package for R: A free software tool for the non-parametric analysis of actigraphy data.
Blume, Christine; Santhi, Nayantara; Schabus, Manuel
2016-01-01
For many studies, participants' sleep-wake patterns are monitored and recorded prior to, during and following an experimental or clinical intervention using actigraphy, i.e. the recording of data generated by movements. Often, these data are merely inspected visually without computation of descriptive parameters, in part due to the lack of user-friendly software. To address this deficit, we developed a package for R Core Team [6], that allows computing several non-parametric measures from actigraphy data. Specifically, it computes the interdaily stability (IS), intradaily variability (IV) and relative amplitude (RA) of activity and gives the start times and average activity values of M10 (i.e. the ten hours with maximal activity) and L5 (i.e. the five hours with least activity). Two functions compute these 'classical' parameters and handle either single or multiple files. Two other functions additionally allow computing an L-value (i.e. the least activity value) for a user-defined time span termed 'Lflex' value. A plotting option is included in all functions. The package can be downloaded from the Comprehensive R Archives Network (CRAN). •The package 'nparACT' for R serves the non-parametric analysis of actigraphy data.•Computed parameters include interdaily stability (IS), intradaily variability (IV) and relative amplitude (RA) as well as start times and average activity during the 10 h with maximal and the 5 h with minimal activity (i.e. M10 and L5).
A novel statistical method for classifying habitat generalists and specialists
DEFF Research Database (Denmark)
Chazdon, Robin L; Chao, Anne; Colwell, Robert K
2011-01-01
We develop a novel statistical approach for classifying generalists and specialists in two distinct habitats. Using a multinomial model based on estimated species relative abundance in two habitats, our method minimizes bias due to differences in sampling intensities between two habitat types...... as well as bias due to insufficient sampling within each habitat. The method permits a robust statistical classification of habitat specialists and generalists, without excluding rare species a priori. Based on a user-defined specialization threshold, the model classifies species into one of four groups...... fraction (57.7%) of bird species with statistical confidence. Based on a conservative specialization threshold and adjustment for multiple comparisons, 64.4% of tree species in the full sample were too rare to classify with confidence. Among the species classified, OG specialists constituted the largest...
Urban Fire Risk Clustering Method Based on Fire Statistics
Institute of Scientific and Technical Information of China (English)
WU Lizhi; REN Aizhu
2008-01-01
Fire statistics and fire analysis have become important ways for us to understand the law of fire,prevent the occurrence of fire, and improve the ability to control fire. According to existing fire statistics, the weighted fire risk calculating method characterized by the number of fire occurrence, direct economic losses,and fire casualties was put forward. On the basis of this method, meanwhile having improved K-mean clus-tering arithmetic, this paper established fire dsk K-mean clustering model, which could better resolve the automatic classifying problems towards fire risk. Fire risk cluster should be classified by the absolute dis-tance of the target instead of the relative distance in the traditional cluster arithmetic. Finally, for applying the established model, this paper carded out fire risk clustering on fire statistics from January 2000 to December 2004 of Shenyang in China. This research would provide technical support for urban fire management.
Statistical methods with applications to demography and life insurance
Khmaladze, Estáte V
2013-01-01
Suitable for statisticians, mathematicians, actuaries, and students interested in the problems of insurance and analysis of lifetimes, Statistical Methods with Applications to Demography and Life Insurance presents contemporary statistical techniques for analyzing life distributions and life insurance problems. It not only contains traditional material but also incorporates new problems and techniques not discussed in existing actuarial literature. The book mainly focuses on the analysis of an individual life and describes statistical methods based on empirical and related processes. Coverage ranges from analyzing the tails of distributions of lifetimes to modeling population dynamics with migrations. To help readers understand the technical points, the text covers topics such as the Stieltjes, Wiener, and Itô integrals. It also introduces other themes of interest in demography, including mixtures of distributions, analysis of longevity and extreme value theory, and the age structure of a population. In addi...
Non-parametric causality detection: An application to social media and financial data
Tsapeli, Fani; Musolesi, Mirco; Tino, Peter
2017-10-01
According to behavioral finance, stock market returns are influenced by emotional, social and psychological factors. Several recent works support this theory by providing evidence of correlation between stock market prices and collective sentiment indexes measured using social media data. However, a pure correlation analysis is not sufficient to prove that stock market returns are influenced by such emotional factors since both stock market prices and collective sentiment may be driven by a third unmeasured factor. Controlling for factors that could influence the study by applying multivariate regression models is challenging given the complexity of stock market data. False assumptions about the linearity or non-linearity of the model and inaccuracies on model specification may result in misleading conclusions. In this work, we propose a novel framework for causal inference that does not require any assumption about a particular parametric form of the model expressing statistical relationships among the variables of the study and can effectively control a large number of observed factors. We apply our method in order to estimate the causal impact that information posted in social media may have on stock market returns of four big companies. Our results indicate that social media data not only correlate with stock market returns but also influence them.
Landslide Susceptibility Statistical Methods: A Critical and Systematic Literature Review
Mihir, Monika; Malamud, Bruce; Rossi, Mauro; Reichenbach, Paola; Ardizzone, Francesca
2014-05-01
Landslide susceptibility assessment, the subject of this systematic review, is aimed at understanding the spatial probability of slope failures under a set of geomorphological and environmental conditions. It is estimated that about 375 landslides that occur globally each year are fatal, with around 4600 people killed per year. Past studies have brought out the increasing cost of landslide damages which primarily can be attributed to human occupation and increased human activities in the vulnerable environments. Many scientists, to evaluate and reduce landslide risk, have made an effort to efficiently map landslide susceptibility using different statistical methods. In this paper, we do a critical and systematic landslide susceptibility literature review, in terms of the different statistical methods used. For each of a broad set of studies reviewed we note: (i) study geography region and areal extent, (ii) landslide types, (iii) inventory type and temporal period covered, (iv) mapping technique (v) thematic variables used (vi) statistical models, (vii) assessment of model skill, (viii) uncertainty assessment methods, (ix) validation methods. We then pulled out broad trends within our review of landslide susceptibility, particularly regarding the statistical methods. We found that the most common statistical methods used in the study of landslide susceptibility include logistic regression, artificial neural network, discriminant analysis and weight of evidence. Although most of the studies we reviewed assessed the model skill, very few assessed model uncertainty. In terms of geographic extent, the largest number of landslide susceptibility zonations were in Turkey, Korea, Spain, Italy and Malaysia. However, there are also many landslides and fatalities in other localities, particularly India, China, Philippines, Nepal and Indonesia, Guatemala, and Pakistan, where there are much fewer landslide susceptibility studies available in the peer-review literature. This
Investigating salt frost scaling by using statistical methods
DEFF Research Database (Denmark)
Hasholt, Marianne Tange; Clemmensen, Line Katrine Harder
2010-01-01
A large data set comprising data for 118 concrete mixes on mix design, air void structure, and the outcome of freeze/thaw testing according to SS 13 72 44 has been analysed by use of statistical methods. The results show that with regard to mix composition, the most important parameter...
Statistical methods for cosmological parameter selection and estimation
Liddle, Andrew R
2009-01-01
The estimation of cosmological parameters from precision observables is an important industry with crucial ramifications for particle physics. This article discusses the statistical methods presently used in cosmological data analysis, highlighting the main assumptions and uncertainties. The topics covered are parameter estimation, model selection, multi-model inference, and experimental design, all primarily from a Bayesian perspective.
Kansas's forests, 2005: statistics, methods, and quality assurance
Patrick D. Miles; W. Keith Moser; Charles J. Barnett
2011-01-01
The first full annual inventory of Kansas's forests was completed in 2005 after 8,868 plots were selected and 468 forested plots were visited and measured. This report includes detailed information on forest inventory methods and data quality estimates. Important resource statistics are included in the tables. A detailed analysis of Kansas inventory is presented...
Optimization of statistical methods impact on quantitative proteomics data
Pursiheimo, A.; Vehmas, A.P.; Afzal, S.; Suomi, T.; Chand, T.; Strauss, L.; Poutanen, M.; Rokka, A.; Corthals, G.L.; Elo, L.L.
2015-01-01
As tools for quantitative label-free mass spectrometry (MS) rapidly develop, a consensus about the best practices is not apparent. In the work described here we compared popular statistical methods for detecting differential protein expression from quantitative MS data using both controlled
Application of statistical methods at copper wire manufacturing
Directory of Open Access Journals (Sweden)
Z. Hajduová
2009-01-01
Full Text Available Six Sigma is a method of management that strives for near perfection. The Six Sigma methodology uses data and rigorous statistical analysis to identify defects in a process or product, reduce variability and achieve as close to zero defects as possible. The paper presents the basic information on this methodology.
Peer-Assisted Learning in Research Methods and Statistics
Stone, Anna; Meade, Claire; Watling, Rosamond
2012-01-01
Feedback from students on a Level 1 Research Methods and Statistics module, studied as a core part of a BSc Psychology programme, highlighted demand for additional tutorials to help them to understand basic concepts. Students in their final year of study commonly request work experience to enhance their employability. All students on the Level 1…
Investigating salt frost scaling by using statistical methods
DEFF Research Database (Denmark)
Hasholt, Marianne Tange; Clemmensen, Line Katrine Harder
2010-01-01
A large data set comprising data for 118 concrete mixes on mix design, air void structure, and the outcome of freeze/thaw testing according to SS 13 72 44 has been analysed by use of statistical methods. The results show that with regard to mix composition, the most important parameter is the equ...
Statistical process control methods for expert system performance monitoring.
Kahn, M G; Bailey, T C; Steib, S A; Fraser, V J; Dunagan, W C
1996-01-01
The literature on the performance evaluation of medical expert system is extensive, yet most of the techniques used in the early stages of system development are inappropriate for deployed expert systems. Because extensive clinical and informatics expertise and resources are required to perform evaluations, efficient yet effective methods of monitoring performance during the long-term maintenance phase of the expert system life cycle must be devised. Statistical process control techniques provide a well-established methodology that can be used to define policies and procedures for continuous, concurrent performance evaluation. Although the field of statistical process control has been developed for monitoring industrial processes, its tools, techniques, and theory are easily transferred to the evaluation of expert systems. Statistical process tools provide convenient visual methods and heuristic guidelines for detecting meaningful changes in expert system performance. The underlying statistical theory provides estimates of the detection capabilities of alternative evaluation strategies. This paper describes a set of statistical process control tools that can be used to monitor the performance of a number of deployed medical expert systems. It describes how p-charts are used in practice to monitor the GermWatcher expert system. The case volume and error rate of GermWatcher are then used to demonstrate how different inspection strategies would perform.
Recent development on statistical methods for personalized medicine discovery.
Zhao, Yingqi; Zeng, Donglin
2013-03-01
It is well documented that patients can show significant heterogeneous responses to treatments so the best treatment strategies may require adaptation over individuals and time. Recently, a number of new statistical methods have been developed to tackle the important problem of estimating personalized treatment rules using single-stage or multiple-stage clinical data. In this paper, we provide an overview of these methods and list a number of challenges.
Yin, Shengwen; Yu, Dejie; Yin, Hui; Lü, Hui; Xia, Baizhan
2017-09-01
Considering the epistemic uncertainties within the hybrid Finite Element/Statistical Energy Analysis (FE/SEA) model when it is used for the response analysis of built-up systems in the mid-frequency range, the hybrid Evidence Theory-based Finite Element/Statistical Energy Analysis (ETFE/SEA) model is established by introducing the evidence theory. Based on the hybrid ETFE/SEA model and the sub-interval perturbation technique, the hybrid Sub-interval Perturbation and Evidence Theory-based Finite Element/Statistical Energy Analysis (SIP-ETFE/SEA) approach is proposed. In the hybrid ETFE/SEA model, the uncertainty in the SEA subsystem is modeled by a non-parametric ensemble, while the uncertainty in the FE subsystem is described by the focal element and basic probability assignment (BPA), and dealt with evidence theory. Within the hybrid SIP-ETFE/SEA approach, the mid-frequency response of interest, such as the ensemble average of the energy response and the cross-spectrum response, is calculated analytically by using the conventional hybrid FE/SEA method. Inspired by the probability theory, the intervals of the mean value, variance and cumulative distribution are used to describe the distribution characteristics of mid-frequency responses of built-up systems with epistemic uncertainties. In order to alleviate the computational burdens for the extreme value analysis, the sub-interval perturbation technique based on the first-order Taylor series expansion is used in ETFE/SEA model to acquire the lower and upper bounds of the mid-frequency responses over each focal element. Three numerical examples are given to illustrate the feasibility and effectiveness of the proposed method.
A new statistical method for mapping QTLs underlying endosperm traits
Institute of Scientific and Technical Information of China (English)
HU Zhiqiu; XU Chenwu
2005-01-01
Genetic expression for an endosperm trait in seeds of cereal crops may be controlled simultaneously by the triploid endosperm genotypes and the diploid maternal genotypes. However, current statistical methods for mapping quantitative trait loci (QTLs) underlying endosperm traits have not been effective in dealing with the putative maternal genetic effects. Combining the quantitative genetic model for diploid maternal traits with triploid endosperm traits, here we propose a new statistical method for mapping QTLs controlling endosperm traits with maternal genetic effects. This method applies the data set of both DNA molecular marker genotypes of each plant in segregation population and the quantitative observations of single endosperms in each plant to map QTL. The maximum likelihood method implemented via the expectation-maximization algorithm was used to the estimate parameters of a putative QTL. Since this method involves the maternal effect that may contribute to endosperm traits, it might be more congruent with the genetics of endosperm traits and more helpful to increasing the precision of QTL mapping. The simulation results show the proposed method provides accurate estimates of the QTL effects and locations with high statistical power.
Using statistical methods of quality management in logistics processes
Directory of Open Access Journals (Sweden)
Tkachenko Alla
2016-04-01
Full Text Available The purpose of the paper is to study the application of statistical methods of logistics process quality management at a large industrial enterprise and testing the theoretical studies. The analysis of the publications shows that a significant number of works by both Ukrainian and foreign authors has been dedicated to the research of quality management, while statistical methods of quality management have only been thoroughly analyzed by a small number of researchers, since these methods are referred to as classical, that is, those that are considered well-known and do not require special attention of modern scholars. In the authors’ opinion, the logistics process is a process of transformation and movement of material and accompanying flows by ensuring management freedom under the conditions of sequential interdependencies; standardization; synchronization; sharing information, and consistency of incentives, using innovative methods and models. In our study, we have shown that the management of logistics processes should use such statistical methods of quality management as descriptive statistics, experiment planning, hypotheses testing, measurement analysis, process opportunities analysis, regression analysis, reliability analysis, sampling, modeling, maps of statistical process control, specification of statistical tolerance, time series analysis. The proposed statistical methods of logistics processes quality management have been tested at the large industrial enterprise JSC "Dniepropetrovsk Aggregate Plant" that specializes in manufacturing hydraulic control valves. The findings suggest that the main purpose in the sphere of logistics processes quality is the continuous improvement of the mining equipment production quality through the use of innovative processes, advanced management systems and information technology. This will enable the enterprise to meet the requirements and expectations of their customers. It has been proved that the
Statistical Properties of Fluctuations: A Method to Check Market Behavior
Panigrahi, Prasanta K; Manimaran, P; Ahalpara, Dilip P
2009-01-01
We analyze the Bombay stock exchange (BSE) price index over the period of last 12 years. Keeping in mind the large fluctuations in last few years, we carefully find out the transient, non-statistical and locally structured variations. For that purpose, we make use of Daubechies wavelet and characterize the fractal behavior of the returns using a recently developed wavelet based fluctuation analysis method. the returns show a fat-tail distribution as also weak non-statistical behavior. We have also carried out continuous wavelet as well as Fourier power spectral analysis to characterize the periodic nature and correlation properties of the time series.
System and method for statistically monitoring and analyzing sensed conditions
Pebay, Philippe P.; Brandt, James M. , Gentile; Ann C. , Marzouk; Youssef M. , Hale; Darrian J. , Thompson; David C.
2010-07-13
A system and method of monitoring and analyzing a plurality of attributes for an alarm condition is disclosed. The attributes are processed and/or unprocessed values of sensed conditions of a collection of a statistically significant number of statistically similar components subjected to varying environmental conditions. The attribute values are used to compute the normal behaviors of some of the attributes and also used to infer parameters of a set of models. Relative probabilities of some attribute values are then computed and used along with the set of models to determine whether an alarm condition is met. The alarm conditions are used to prevent or reduce the impact of impending failure.
From Microphysics to Macrophysics Methods and Applications of Statistical Physics
Balian, Roger
2007-01-01
This text not only provides a thorough introduction to statistical physics and thermodynamics but also exhibits the universality of the chain of ideas that leads from the laws of microphysics to the macroscopic behaviour of matter. A wide range of applications teaches students how to make use of the concepts, and many exercises will help to deepen their understanding. Drawing on both quantum mechanics and classical physics, the book follows modern research in statistical physics. Volume I discusses in detail the probabilistic description of quantum or classical systems, the Boltzmann-Gibbs distributions, the conservation laws, and the interpretation of entropy as missing information. Thermodynamics and electromagnetism in matter are dealt with, as well as applications to gases, both dilute and condensed, and to phase transitions. Volume II applies statistical methods to systems governed by quantum effects, in particular to solid state physics, explaining properties due to the crystal structure or to the latti...
Applied statistical methods in agriculture, health and life sciences
Lawal, Bayo
2014-01-01
This textbook teaches crucial statistical methods to answer research questions using a unique range of statistical software programs, including MINITAB and R. This textbook is developed for undergraduate students in agriculture, nursing, biology and biomedical research. Graduate students will also find it to be a useful way to refresh their statistics skills and to reference software options. The unique combination of examples is approached using MINITAB and R for their individual strengths. Subjects covered include among others data description, probability distributions, experimental design, regression analysis, randomized design and biological assay. Unlike other biostatistics textbooks, this text also includes outliers, influential observations in regression and an introduction to survival analysis. Material is taken from the author's extensive teaching and research in Africa, USA and the UK. Sample problems, references and electronic supplementary material accompany each chapter.
Predicting recreational water quality advisories: A comparison of statistical methods
Brooks, Wesley R.; Corsi, Steven R.; Fienen, Michael N.; Carvin, Rebecca B.
2016-01-01
Epidemiological studies indicate that fecal indicator bacteria (FIB) in beach water are associated with illnesses among people having contact with the water. In order to mitigate public health impacts, many beaches are posted with an advisory when the concentration of FIB exceeds a beach action value. The most commonly used method of measuring FIB concentration takes 18–24 h before returning a result. In order to avoid the 24 h lag, it has become common to ”nowcast” the FIB concentration using statistical regressions on environmental surrogate variables. Most commonly, nowcast models are estimated using ordinary least squares regression, but other regression methods from the statistical and machine learning literature are sometimes used. This study compares 14 regression methods across 7 Wisconsin beaches to identify which consistently produces the most accurate predictions. A random forest model is identified as the most accurate, followed by multiple regression fit using the adaptive LASSO.
Statistical disclosure control for microdata methods and applications in R
Templ, Matthias
2017-01-01
This book on statistical disclosure control presents the theory, applications and software implementation of the traditional approach to (micro)data anonymization, including data perturbation methods, disclosure risk, data utility, information loss and methods for simulating synthetic data. Introducing readers to the R packages sdcMicro and simPop, the book also features numerous examples and exercises with solutions, as well as case studies with real-world data, accompanied by the underlying R code to allow readers to reproduce all results. The demand for and volume of data from surveys, registers or other sources containing sensible information on persons or enterprises have increased significantly over the last several years. At the same time, privacy protection principles and regulations have imposed restrictions on the access and use of individual data. Proper and secure microdata dissemination calls for the application of statistical disclosure control methods to the data before release. This book is in...
Applications of quantum entropy to statistics
Energy Technology Data Exchange (ETDEWEB)
Silver, R.N.; Martz, H.F.
1994-07-01
This paper develops two generalizations of the maximum entropy (ME) principle. First, Shannon classical entropy is replaced by von Neumann quantum entropy to yield a broader class of information divergences (or penalty functions) for statistics applications. Negative relative quantum entropy enforces convexity, positivity, non-local extensivity and prior correlations such as smoothness. This enables the extension of ME methods from their traditional domain of ill-posed in-verse problems to new applications such as non-parametric density estimation. Second, given a choice of information divergence, a combination of ME and Bayes rule is used to assign both prior and posterior probabilities. Hyperparameters are interpreted as Lagrange multipliers enforcing constraints. Conservation principles are proposed to act statistical regularization and other hyperparameters, such as conservation of information and smoothness. ME provides an alternative to heirarchical Bayes methods.
Alternative statistical methods for cytogenetic radiation biological dosimetry
Fornalski, Krzysztof Wojciech
2014-01-01
The paper presents alternative statistical methods for biological dosimetry, such as the Bayesian and Monte Carlo method. The classical Gaussian and robust Bayesian fit algorithms for the linear, linear-quadratic as well as saturated and critical calibration curves are described. The Bayesian model selection algorithm for those curves is also presented. In addition, five methods of dose estimation for a mixed neutron and gamma irradiation field were described: two classical methods, two Bayesian methods and one Monte Carlo method. Bayesian methods were also enhanced and generalized for situations with many types of mixed radiation. All algorithms were presented in easy-to-use form, which can be applied to any computational programming language. The presented algorithm is universal, although it was originally dedicated to cytogenetic biological dosimetry of victims of a nuclear reactor accident.
Statistical methods for assessing agreement between continuous measurements
DEFF Research Database (Denmark)
Sokolowski, Ineta; Hansen, Rikke Pilegaard; Vedsted, Peter
), concordance coefficient, Bland-Altman limits of agreement and percentage of agreement to assess the agreement between patient reported delay and doctor reported delay in diagnosis of cancer in general practice. Key messages: The correct statistical approach is not obvious. Many studies give the product......-moment correlation coefficient (r) between the results of the two measurements methods as an indicator of agreement, which is wrong. There have been proposed several alternative methods, which we will describe together with preconditions for use of the methods....
Statistical methods of SNP data analysis with applications
Bulinski, Alexander; Shashkin, Alexey; Yaskov, Pavel
2011-01-01
Various statistical methods important for genetic analysis are considered and developed. Namely, we concentrate on the multifactor dimensionality reduction, logic regression, random forests and stochastic gradient boosting. These methods and their new modifications, e.g., the MDR method with "independent rule", are used to study the risk of complex diseases such as cardiovascular ones. The roles of certain combinations of single nucleotide polymorphisms and external risk factors are examined. To perform the data analysis concerning the ischemic heart disease and myocardial infarction the supercomputer SKIF "Chebyshev" of the Lomonosov Moscow State University was employed.
Identifying Reflectors in Seismic Images via Statistic and Syntactic Methods
Directory of Open Access Journals (Sweden)
Carlos A. Perez
2010-04-01
Full Text Available In geologic interpretation of seismic reflection data, accurate identification of reflectors is the foremost step to ensure proper subsurface structural definition. Reflector information, along with other data sets, is a key factor to predict the presence of hydrocarbons. In this work, mathematic and pattern recognition theory was adapted to design two statistical and two syntactic algorithms which constitute a tool in semiautomatic reflector identification. The interpretive power of these four schemes was evaluated in terms of prediction accuracy and computational speed. Among these, the semblance method was confirmed to render the greatest accuracy and speed. Syntactic methods offer an interesting alternative due to their inherently structural search method.
Applied systems ecology: models, data, and statistical methods
Energy Technology Data Exchange (ETDEWEB)
Eberhardt, L L
1976-01-01
In this report, systems ecology is largely equated to mathematical or computer simulation modelling. The need for models in ecology stems from the necessity to have an integrative device for the diversity of ecological data, much of which is observational, rather than experimental, as well as from the present lack of a theoretical structure for ecology. Different objectives in applied studies require specialized methods. The best predictive devices may be regression equations, often non-linear in form, extracted from much more detailed models. A variety of statistical aspects of modelling, including sampling, are discussed. Several aspects of population dynamics and food-chain kinetics are described, and it is suggested that the two presently separated approaches should be combined into a single theoretical framework. It is concluded that future efforts in systems ecology should emphasize actual data and statistical methods, as well as modelling.
Statistical methods for detecting differentially methylated loci and regions
Directory of Open Access Journals (Sweden)
Mark D Robinson
2014-09-01
Full Text Available DNA methylation, the reversible addition of methyl groups at CpG dinucleotides, represents an important regulatory layer associated with gene expression. Changed methylation status has been noted across diverse pathological states, including cancer. The rapid development and uptake of microarrays and large scale DNA sequencing has prompted an explosion of data analytic methods for processing and discovering changes in DNA methylation across varied data types. In this mini-review, we present a compact and accessible discussion of many of the salient challenges, such as experimental design, statistical methods for differential methylation detection, critical considerations such as cell type composition and the potential confounding that can arise from batch effects. From a statistical perspective, our main interests include the use of empirical Bayes or hierarchical models, which have proved immensely powerful in genomics, and the procedures by which false discovery control is achieved.
An Alternating Iterative Method and Its Application in Statistical Inference
Institute of Scientific and Technical Information of China (English)
Ning Zhong SHI; Guo Rong HU; Qing CUI
2008-01-01
This paper studies non-convex programming problems. It is known that, in statistical inference, many constrained estimation problems may be expressed as convex programming problems. However, in many practical problems, the objective functions are not convex. In this paper, we give a definition of a semi-convex objective function and discuss the corresponding non-convex programming problems. A two-step iterative algorithm called the alternating iterative method is proposed for finding solutions for such problems. The method is illustrated by three examples in constrained estimation problems given in Sasabuchi et al. (Biometrika, 72, 465–472 (1983)), Shi N. Z. (J. Multivariate Anal.,50, 282–293 (1994)) and El Barmi H. and Dykstra R. (Ann. Statist., 26, 1878–1893 (1998)).
New Graphical Methods and Test Statistics for Testing Composite Normality
Directory of Open Access Journals (Sweden)
Marc S. Paolella
2015-07-01
Full Text Available Several graphical methods for testing univariate composite normality from an i.i.d. sample are presented. They are endowed with correct simultaneous error bounds and yield size-correct tests. As all are based on the empirical CDF, they are also consistent for all alternatives. For one test, called the modified stabilized probability test, or MSP, a highly simplified computational method is derived, which delivers the test statistic and also a highly accurate p-value approximation, essentially instantaneously. The MSP test is demonstrated to have higher power against asymmetric alternatives than the well-known and powerful Jarque-Bera test. A further size-correct test, based on combining two test statistics, is shown to have yet higher power. The methodology employed is fully general and can be applied to any i.i.d. univariate continuous distribution setting.
Multivariate Statistical Process Control Process Monitoring Methods and Applications
Ge, Zhiqiang
2013-01-01
Given their key position in the process control industry, process monitoring techniques have been extensively investigated by industrial practitioners and academic control researchers. Multivariate statistical process control (MSPC) is one of the most popular data-based methods for process monitoring and is widely used in various industrial areas. Effective routines for process monitoring can help operators run industrial processes efficiently at the same time as maintaining high product quality. Multivariate Statistical Process Control reviews the developments and improvements that have been made to MSPC over the last decade, and goes on to propose a series of new MSPC-based approaches for complex process monitoring. These new methods are demonstrated in several case studies from the chemical, biological, and semiconductor industrial areas. Control and process engineers, and academic researchers in the process monitoring, process control and fault detection and isolation (FDI) disciplines will be inter...
Multivariate methods and forecasting with IBM SPSS statistics
Aljandali, Abdulkader
2017-01-01
This is the second of a two-part guide to quantitative analysis using the IBM SPSS Statistics software package; this volume focuses on multivariate statistical methods and advanced forecasting techniques. More often than not, regression models involve more than one independent variable. For example, forecasting methods are commonly applied to aggregates such as inflation rates, unemployment, exchange rates, etc., that have complex relationships with determining variables. This book introduces multivariate regression models and provides examples to help understand theory underpinning the model. The book presents the fundamentals of multivariate regression and then moves on to examine several related techniques that have application in business-orientated fields such as logistic and multinomial regression. Forecasting tools such as the Box-Jenkins approach to time series modeling are introduced, as well as exponential smoothing and naïve techniques. This part also covers hot topics such as Factor Analysis, Dis...
Statistical Methods for Thermonuclear Reaction Rates and Nucleosynthesis Simulations
Iliadis, Christian; Coc, Alain; Timmes, F X; Champagne, Art E
2014-01-01
Rigorous statistical methods for estimating thermonuclear reaction rates and nucleosynthesis are becoming increasingly established in nuclear astrophysics. The main challenge being faced is that experimental reaction rates are highly complex quantities derived from a multitude of different measured nuclear parameters (e.g., astrophysical S-factors, resonance energies and strengths, particle and gamma-ray partial widths). We discuss the application of the Monte Carlo method to two distinct, but related, questions. First, given a set of measured nuclear parameters, how can one best estimate the resulting thermonuclear reaction rates and associated uncertainties? Second, given a set of appropriate reaction rates, how can one best estimate the abundances from nucleosynthesis (i.e., reaction network) calculations? The techniques described here provide probability density functions that can be used to derive statistically meaningful reaction rates and final abundances for any desired coverage probability. Examples ...
Statistical methods for longitudinal data with agricultural applications
DEFF Research Database (Denmark)
Anantharama Ankinakatte, Smitha
The PhD study focuses on modeling two kings of longitudinal data arising in agricultural applications: continuous time series data and discrete longitudinal data. Firstly, two statistical methods, neural networks and generalized additive models, are applied to predict masistis using multivariate...... algorithm. This was found to compare favourably with the algorithm implemented in the well-known Beagle software. Finally, an R package to apply APFA models developed as part of the PhD project is described...
Diametral creep prediction of pressure tube using statistical regression methods
Energy Technology Data Exchange (ETDEWEB)
Kim, D. [Korea Advanced Inst. of Science and Technology, Daejeon (Korea, Republic of); Lee, J.Y. [Korea Electric Power Research Inst., Daejeon (Korea, Republic of); Na, M.G. [Chosun Univ., Gwangju (Korea, Republic of); Jang, C. [Korea Advanced Inst. of Science and Technology, Daejeon (Korea, Republic of)
2010-07-01
Diametral creep prediction of pressure tube in CANDU reactor is an important factor for ROPT calculation. In this study, pressure tube diametral creep prediction models were developed using statistical regression method such as linear mixed model for longitudinal data analysis. Inspection and operating condition data of Wolsong unit 1 and 2 reactors were used. Serial correlation model and random coefficient model were developed for pressure tube diameter prediction. Random coefficient model provided more accurate results than serial correlation model. (author)
Goshtasbpour, Mehrdad
2014-01-01
By singular value decomposition (SVD) of a numerically singular Hessian matrix and a numerically singular system of linear equations for the experimental data (accumulated in the respective ${\\chi ^2}$ function) and constraints, least square solutions and their propagated errors for the non-parametric extraction of Partons from $F_2$ are obtained. SVD and its physical application is phenomenologically described in the two cases. Among the subjects covered are: identification and properties of the boundary between the two subsets of ordered eigenvalues corresponding to range and null space, and the eigenvalue structure of the null space of the singular matrix, including a second boundary separating the smallest eigenvalues of essentially no information, in a particular case. The eigenvector-eigenvalue structure of "redundancy and smallness" of the errors of two pdf sets, in our simplified Hessian model, is described by a secondary manifestation of deeper null space, in the context of SVD.
Chakrabarty, Dalia
2013-01-01
In lieu of direct detection of dark matter, estimation of the distribution of the gravitational mass in distant galaxies is of crucial importance in Astrophysics. Typically, such estimation is performed using small samples of noisy, partially missing measurements - only some of the three components of the velocity and location vectors of individual particles that live in the galaxy are measurable. Such limitations of the available data in turn demands that simplifying model assumptions be undertaken. Thus, assuming that the phase space of a galaxy manifests simple symmetries - such as isotropy - allows for the learning of the density of the gravitational mass in galaxies. This is equivalent to assuming that the phase space $pdf$ from which the velocity and location vectors of galactic particles are sampled from, is an isotropic function of these vectors. We present a new non-parametric test of hypothesis that tests for relative support in two or more measured data sets of disparate sizes, for the undertaken m...
DEFF Research Database (Denmark)
Petersen, Jørgen Holm
2009-01-01
A conceptually simple two-dimensional conditional reference curve is described. The curve gives a decision basis for determining whether a bivariate response from an individual is "normal" or "abnormal" when taking into account that a third (conditioning) variable may influence the bivariate...... response. The reference curve is not only characterized analytically but also by geometric properties that are easily communicated to medical doctors - the users of such curves. The reference curve estimator is completely non-parametric, so no distributional assumptions are needed about the two......-dimensional response. An example that will serve to motivate and illustrate the reference is the study of the height/weight distribution of 7-8-year-old Danish school girls born in 1930, 1950, or 1970....
Statistical method for detecting structural change in the growth process.
Ninomiya, Yoshiyuki; Yoshimoto, Atsushi
2008-03-01
Due to competition among individual trees and other exogenous factors that change the growth environment, each tree grows following its own growth trend with some structural changes in growth over time. In the present article, a new method is proposed to detect a structural change in the growth process. We formulate the method as a simple statistical test for signal detection without constructing any specific model for the structural change. To evaluate the p-value of the test, the tube method is developed because the regular distribution theory is insufficient. Using two sets of tree diameter growth data sampled from planted forest stands of Cryptomeria japonica in Japan, we conduct an analysis of identifying the effect of thinning on the growth process as a structural change. Our results demonstrate that the proposed method is useful to identify the structural change caused by thinning. We also provide the properties of the method in terms of the size and power of the test.
Microprocessors as an Adjunct to Statistics Instruction.
Miller, William G.
Examinations of costs and acquisition of facilities indicate that an Altair 8800A microcomputer with a program library of parametric, non-parametric, mathematical, and teaching programs can be used effectively for teaching college-level statistics. Statistical packages presently in use require extensive computing knowledge beyond the students' and…
Hayslett, H T
1991-01-01
Statistics covers the basic principles of Statistics. The book starts by tackling the importance and the two kinds of statistics; the presentation of sample data; the definition, illustration and explanation of several measures of location; and the measures of variation. The text then discusses elementary probability, the normal distribution and the normal approximation to the binomial. Testing of statistical hypotheses and tests of hypotheses about the theoretical proportion of successes in a binomial population and about the theoretical mean of a normal population are explained. The text the
The analysis of variance in anaesthetic research: statistics, biography and history.
Pandit, J J
2010-12-01
Multiple t-tests (or their non-parametric equivalents) are often used erroneously to compare the means of three or more groups in anaesthetic research. Methods for correcting the p value regarded as significant can be applied to take account of multiple testing, but these are somewhat arbitrary and do not avoid several unwieldy calculations. The appropriate method for most such comparisons is the 'analysis of variance' that not only economises on the number of statistical procedures, but also indicates if underlying factors or sub-groups have contributed to any significant results. This article outlines the history, rationale and method of this analysis.
Postnikov, Sergey; Hernandez, Xavier; Capozziello, Salvatore
2014-01-01
We study the dark energy equation of state as a function of redshift in a non-parametric way, without imposing any {\\it a priori} $w(z)$ (ratio of pressure over energy density) functional form. As a check of the method, we test our scheme through the use of synthetic data sets produced from different input cosmological models which have the same relative errors and redshift distribution as the real data. Using the luminosity-time $L_{X}-T_{a}$ correlation for GRB X-ray afterglows (the Dainotti et al. correlation), we are able to utilize GRB sample from the {\\it Swift} satellite as probes of the expansion history of the Universe out to $z \\approx 10$. Within the assumption of a flat FLRW universe and combining SNeIa data with BAO constraints, the resulting maximum likelihood solutions are close to a constant $w=-1$. If one imposes the restriction of a constant $w$, we obtain $w=-0.99 \\pm 0.06$ (consistent with a cosmological constant) with the present day Hubble constant as $H_{0}=70.0 \\pm 0.6$ ${\\rm km} \\, {\\...
Literature in Focus: Statistical Methods in Experimental Physics
2007-01-01
Frederick James was a high-energy physicist who became the CERN "expert" on statistics and is now well-known around the world, in part for this famous text. The first edition of Statistical Methods in Experimental Physics was originally co-written with four other authors and was published in 1971 by North Holland (now an imprint of Elsevier). It became such an important text that demand for it has continued for more than 30 years. Fred has updated it and it was released in a second edition by World Scientific in 2006. It is still a top seller and there is no exaggeration in calling it «the» reference on the subject. A full review of the title appeared in the October CERN Courier.Come and meet the author to hear more about how this book has flourished during its 35-year lifetime. Frederick James Statistical Methods in Experimental Physics Monday, 26th of November, 4 p.m. Council Chamber (Bldg. 503-1-001) The author will be introduced...
Fragment Identification and Statistics Method of Hypervelocity Impact SPH Simulation
Institute of Scientific and Technical Information of China (English)
ZHANG Xiaotian; JIA Guanghui; HUANG Hai
2011-01-01
A comprehensive treatment to the fragment identification and statistics for the smoothed particle hydrodynamics (SPH) simulation of hypervelocity impact is presented.Based on SPH method, combined with finite element method (FEM), the computation is performed.The fragments are identified by a new pre- and post-processing algorithm and then converted into a binary graph.The number of fragments and the attached SPH particles are determined by counting the quantity of connected domains on the binary graph.The size, velocity vector and mass of each fragment are calculated by the particles' summation and weighted average.The dependence of this method on finite element edge length and simulation terminal time is discussed.An example of tungsten rods impacting steel plates is given for calibration.The computation results match experiments well and demonstrate the effectiveness of this method.
Quantitative EEG Applying the Statistical Recognition Pattern Method
DEFF Research Database (Denmark)
Engedal, Knut; Snaedal, Jon; Hoegh, Peter
2015-01-01
BACKGROUND/AIM: The aim of this study was to examine the discriminatory power of quantitative EEG (qEEG) applying the statistical pattern recognition (SPR) method to separate Alzheimer's disease (AD) patients from elderly individuals without dementia and from other dementia patients. METHODS...... accepted criteria by at least 2 clinicians. EEGs were recorded in a standardized way and analyzed independently of the clinical diagnoses, using the SPR method. RESULTS: In receiver operating characteristic curve analyses, the qEEGs separated AD patients from healthy elderly individuals with an area under...... the curve (AUC) of 0.90, representing a sensitivity of 84% and a specificity of 81%. The qEEGs further separated patients with Lewy body dementia or Parkinson's disease dementia from AD patients with an AUC of 0.9, a sensitivity of 85% and a specificity of 87%. CONCLUSION: qEEG using the SPR method could...
A review of statistical methods for preprocessing oligonucleotide microarrays.
Wu, Zhijin
2009-12-01
Microarrays have become an indispensable tool in biomedical research. This powerful technology not only makes it possible to quantify a large number of nucleic acid molecules simultaneously, but also produces data with many sources of noise. A number of preprocessing steps are therefore necessary to convert the raw data, usually in the form of hybridisation images, to measures of biological meaning that can be used in further statistical analysis. Preprocessing of oligonucleotide arrays includes image processing, background adjustment, data normalisation/transformation and sometimes summarisation when multiple probes are used to target one genomic unit. In this article, we review the issues encountered in each preprocessing step and introduce the statistical models and methods in preprocessing.
Directory of Open Access Journals (Sweden)
Wang Xiaoqiang
2012-04-01
Full Text Available Abstract Background Quantitative trait loci (QTL detection on a huge amount of phenotypes, like eQTL detection on transcriptomic data, can be dramatically impaired by the statistical properties of interval mapping methods. One of these major outcomes is the high number of QTL detected at marker locations. The present study aims at identifying and specifying the sources of this bias, in particular in the case of analysis of data issued from outbred populations. Analytical developments were carried out in a backcross situation in order to specify the bias and to propose an algorithm to control it. The outbred population context was studied through simulated data sets in a wide range of situations. The likelihood ratio test was firstly analyzed under the "one QTL" hypothesis in a backcross population. Designs of sib families were then simulated and analyzed using the QTL Map software. On the basis of the theoretical results in backcross, parameters such as the population size, the density of the genetic map, the QTL effect and the true location of the QTL, were taken into account under the "no QTL" and the "one QTL" hypotheses. A combination of two non parametric tests - the Kolmogorov-Smirnov test and the Mann-Whitney-Wilcoxon test - was used in order to identify the parameters that affected the bias and to specify how much they influenced the estimation of QTL location. Results A theoretical expression of the bias of the estimated QTL location was obtained for a backcross type population. We demonstrated a common source of bias under the "no QTL" and the "one QTL" hypotheses and qualified the possible influence of several parameters. Simulation studies confirmed that the bias exists in outbred populations under both the hypotheses of "no QTL" and "one QTL" on a linkage group. The QTL location was systematically closer to marker locations than expected, particularly in the case of low QTL effect, small population size or low density of markers, i
Mathematical and statistical methods for actuarial sciences and finance
Sibillo, Marilena
2014-01-01
The interaction between mathematicians and statisticians working in the actuarial and financial fields is producing numerous meaningful scientific results. This volume, comprising a series of four-page papers, gathers new ideas relating to mathematical and statistical methods in the actuarial sciences and finance. The book covers a variety of topics of interest from both theoretical and applied perspectives, including: actuarial models; alternative testing approaches; behavioral finance; clustering techniques; coherent and non-coherent risk measures; credit-scoring approaches; data envelopment analysis; dynamic stochastic programming; financial contagion models; financial ratios; intelligent financial trading systems; mixture normality approaches; Monte Carlo-based methodologies; multicriteria methods; nonlinear parameter estimation techniques; nonlinear threshold models; particle swarm optimization; performance measures; portfolio optimization; pricing methods for structured and non-structured derivatives; r...
Evolutionary Computation Methods and their applications in Statistics
Directory of Open Access Journals (Sweden)
Francesco Battaglia
2013-05-01
Full Text Available A brief discussion of the genesis of evolutionary computation methods, their relationship to artificial intelligence, and the contribution of genetics and Darwin’s theory of natural evolution is provided. Then, the main evolutionary computation methods are illustrated: evolution strategies, genetic algorithms, estimation of distribution algorithms, differential evolution, and a brief description of some evolutionary behavior methods such as ant colony and particle swarm optimization. We also discuss the role of the genetic algorithm for multivariate probability distribution random generation, rather than as a function optimizer. Finally, some relevant applications of genetic algorithm to statistical problems are reviewed: selection of variables in regression, time series model building, outlier identification, cluster analysis, design of experiments.
Links to sources of cancer-related statistics, including the Surveillance, Epidemiology and End Results (SEER) Program, SEER-Medicare datasets, cancer survivor prevalence data, and the Cancer Trends Progress Report.
Basic statistical tools in research and data analysis
Ali, Zulfiqar; Bhaskar, S Bala
2016-01-01
Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data analysis.
Basic statistical tools in research and data analysis.
Ali, Zulfiqar; Bhaskar, S Bala
2016-09-01
Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data analysis.
Basic statistical tools in research and data analysis
Directory of Open Access Journals (Sweden)
Zulfiqar Ali
2016-01-01
Full Text Available Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data analysis.
Bayesian statistic methods and theri application in probabilistic simulation models
Directory of Open Access Journals (Sweden)
Sergio Iannazzo
2007-03-01
Full Text Available Bayesian statistic methods are facing a rapidly growing level of interest and acceptance in the field of health economics. The reasons of this success are probably to be found on the theoretical fundaments of the discipline that make these techniques more appealing to decision analysis. To this point should be added the modern IT progress that has developed different flexible and powerful statistical software framework. Among them probably one of the most noticeably is the BUGS language project and its standalone application for MS Windows WinBUGS. Scope of this paper is to introduce the subject and to show some interesting applications of WinBUGS in developing complex economical models based on Markov chains. The advantages of this approach reside on the elegance of the code produced and in its capability to easily develop probabilistic simulations. Moreover an example of the integration of bayesian inference models in a Markov model is shown. This last feature let the analyst conduce statistical analyses on the available sources of evidence and exploit them directly as inputs in the economic model.
Application of Statistical Process Control Methods for IDS
Directory of Open Access Journals (Sweden)
Muhammad Sadiq Ali Khan
2012-11-01
Full Text Available As technology improves, attackers are trying to get access to the network system resources by so many means. Open loop holes in the network allow them to penetrate in the network more easily; statistical methods have great importance in the area of computer and network security, in detecting the malfunctioning of the network system. Development of internet security solution needed to protect the system and to with stand prolonged and diverse attack. In this paper Statistical approach has been used, conventionally Statistical Control Charts has been used for quality characteristics however in IDS abnormal access can be easily detected and appropriate control limit can be established. Two different charts are investigated and Shewhart chart based on average has produced better accuracy. The approach used here for intrusion detection in such a way that if the data packet is drastically different from normal variation then it can be classified as attack. In other words a system variation may be due to some special reason. If these causes are investigated then natural variation and abnormal variation can be distinguished which can be used for distinction of behaviors of the system.
Statistical and Mathematical Methods for Synoptic Time Domain Surveys
Mahabal, Ashish A.; SAMSI Synoptic Surveys Time Domain Working Group
2017-01-01
Recent advances in detector technology, electronics, data storage, and computation have enabled astronomers to collect larger and larger datasets, and moreover, pose interesting questions to answer with those data. The complexity of the data allows data science techniques to be used. These have to be grounded in sound techniques. Identify interesting mathematical and statistical challenges and working on their solutions is one of the aims of the year-long ‘Statistical, Mathematical and Computational Methods for Astronomy (ASTRO)’ program of SAMSI. Of the many working groups that have been formed, one is on Synoptic Time Domain Surveys. Within this we have various subgroups discussing topics such as Designing Statistical Features for Optimal Classification, Scheduling Observations, Incorporating Unstructured Information, Detecting Outliers, Lightcurve Decomposition and Interpolation, Domain Adaptation, and also Designing a Data Challenge. We will briefly highlight some of the work going on in these subgroups along with their interconnections, and the plans for the near future. We will also highlight the overlaps with the other SAMSI working groups and also indicate how the wider astronomy community can both participate and benefit from the activities.
Statistical analysis of the precision of the Match method
Directory of Open Access Journals (Sweden)
R. Lehmann
2005-05-01
Full Text Available The Match method quantifies chemical ozone loss in the polar stratosphere. The basic idea consists in calculating the forward trajectory of an air parcel that has been probed by an ozone measurement (e.g., by an ozone sonde or satellite and finding a second ozone measurement close to this trajectory. Such an event is called a ''match''. A rate of chemical ozone destruction can be obtained by a statistical analysis of several tens of such match events. Information on the uncertainty of the calculated rate can be inferred from the scatter of the ozone mixing ratio difference (second measurement minus first measurement associated with individual matches. A standard analysis would assume that the errors of these differences are statistically independent. However, this assumption may be violated because different matches can share a common ozone measurement, so that the errors associated with these match events become statistically dependent. Taking this effect into account, we present an analysis of the uncertainty of the final Match result. It has been applied to Match data from the Arctic winters 1995, 1996, 2000, and 2003. For these ozone-sonde Match studies the effect of the error correlation on the uncertainty estimates is rather small: compared to a standard error analysis, the uncertainty estimates increase by 15% on average. However, the effect is more pronounced for typical satellite Match analyses: for an Antarctic satellite Match study (2003, the uncertainty estimates increase by 60% on average.
Hybrid perturbation methods based on statistical time series models
San-Juan, Juan Félix; San-Martín, Montserrat; Pérez, Iván; López, Rosario
2016-04-01
In this work we present a new methodology for orbit propagation, the hybrid perturbation theory, based on the combination of an integration method and a prediction technique. The former, which can be a numerical, analytical or semianalytical theory, generates an initial approximation that contains some inaccuracies derived from the fact that, in order to simplify the expressions and subsequent computations, not all the involved forces are taken into account and only low-order terms are considered, not to mention the fact that mathematical models of perturbations not always reproduce physical phenomena with absolute precision. The prediction technique, which can be based on either statistical time series models or computational intelligence methods, is aimed at modelling and reproducing missing dynamics in the previously integrated approximation. This combination results in the precision improvement of conventional numerical, analytical and semianalytical theories for determining the position and velocity of any artificial satellite or space debris object. In order to validate this methodology, we present a family of three hybrid orbit propagators formed by the combination of three different orders of approximation of an analytical theory and a statistical time series model, and analyse their capability to process the effect produced by the flattening of the Earth. The three considered analytical components are the integration of the Kepler problem, a first-order and a second-order analytical theories, whereas the prediction technique is the same in the three cases, namely an additive Holt-Winters method.
Classification of Specialized Farms Applying Multivariate Statistical Methods
Directory of Open Access Journals (Sweden)
Zuzana Hloušková
2017-01-01
Full Text Available Classification of specialized farms applying multivariate statistical methods The paper is aimed at application of advanced multivariate statistical methods when classifying cattle breeding farming enterprises by their economic size. Advantage of the model is its ability to use a few selected indicators compared to the complex methodology of current classification model that requires knowledge of detailed structure of the herd turnover and structure of cultivated crops. Output of the paper is intended to be applied within farm structure research focused on future development of Czech agriculture. As data source, the farming enterprises database for 2014 has been used, from the FADN CZ system. The predictive model proposed exploits knowledge of actual size classes of the farms tested. Outcomes of the linear discriminatory analysis multifactor classification method have supported the chance of filing farming enterprises in the group of Small farms (98 % filed correctly, and the Large and Very Large enterprises (100 % filed correctly. The Medium Size farms have been correctly filed at 58.11 % only. Partial shortages of the process presented have been found when discriminating Medium and Small farms.
Optimization of Statistical Methods Impact on Quantitative Proteomics Data.
Pursiheimo, Anna; Vehmas, Anni P; Afzal, Saira; Suomi, Tomi; Chand, Thaman; Strauss, Leena; Poutanen, Matti; Rokka, Anne; Corthals, Garry L; Elo, Laura L
2015-10-02
As tools for quantitative label-free mass spectrometry (MS) rapidly develop, a consensus about the best practices is not apparent. In the work described here we compared popular statistical methods for detecting differential protein expression from quantitative MS data using both controlled experiments with known quantitative differences for specific proteins used as standards as well as "real" experiments where differences in protein abundance are not known a priori. Our results suggest that data-driven reproducibility-optimization can consistently produce reliable differential expression rankings for label-free proteome tools and are straightforward in their application.
Estimated Accuracy of Three Common Trajectory Statistical Methods
Kabashnikov, Vitaliy P.; Chaikovsky, Anatoli P.; Kucsera, Tom L.; Metelskaya, Natalia S.
2011-01-01
Three well-known trajectory statistical methods (TSMs), namely concentration field (CF), concentration weighted trajectory (CWT), and potential source contribution function (PSCF) methods were tested using known sources and artificially generated data sets to determine the ability of TSMs to reproduce spatial distribution of the sources. In the works by other authors, the accuracy of the trajectory statistical methods was estimated for particular species and at specified receptor locations. We have obtained a more general statistical estimation of the accuracy of source reconstruction and have found optimum conditions to reconstruct source distributions of atmospheric trace substances. Only virtual pollutants of the primary type were considered. In real world experiments, TSMs are intended for application to a priori unknown sources. Therefore, the accuracy of TSMs has to be tested with all possible spatial distributions of sources. An ensemble of geographical distributions of virtual sources was generated. Spearman s rank order correlation coefficient between spatial distributions of the known virtual and the reconstructed sources was taken to be a quantitative measure of the accuracy. Statistical estimates of the mean correlation coefficient and a range of the most probable values of correlation coefficients were obtained. All the TSMs that were considered here showed similar close results. The maximum of the ratio of the mean correlation to the width of the correlation interval containing the most probable correlation values determines the optimum conditions for reconstruction. An optimal geographical domain roughly coincides with the area supplying most of the substance to the receptor. The optimal domain s size is dependent on the substance decay time. Under optimum reconstruction conditions, the mean correlation coefficients can reach 0.70 0.75. The boundaries of the interval with the most probable correlation values are 0.6 0.9 for the decay time of 240 h
Concepts and methods in modern theoretical chemistry statistical mechanics
Ghosh, Swapan Kumar
2013-01-01
Concepts and Methods in Modern Theoretical Chemistry: Statistical Mechanics, the second book in a two-volume set, focuses on the dynamics of systems and phenomena. A new addition to the series Atoms, Molecules, and Clusters, this book offers chapters written by experts in their fields. It enables readers to learn how concepts from ab initio quantum chemistry and density functional theory (DFT) can be used to describe, understand, and predict chemical dynamics. This book covers a wide range of subjects, including discussions on the following topics: Time-dependent DFT Quantum fluid dynamics (QF
Ruppin, F.; Adam, R.; Comis, B.; Ade, P.; André, P.; Arnaud, M.; Beelen, A.; Benoît, A.; Bideaud, A.; Billot, N.; Bourrion, O.; Calvo, M.; Catalano, A.; Coiffard, G.; D'Addabbo, A.; De Petris, M.; Désert, F.-X.; Doyle, S.; Goupy, J.; Kramer, C.; Leclercq, S.; Macías-Pérez, J. F.; Mauskopf, P.; Mayet, F.; Monfardini, A.; Pajot, F.; Pascale, E.; Perotto, L.; Pisano, G.; Pointecouteau, E.; Ponthieu, N.; Pratt, G. W.; Revéret, V.; Ritacco, A.; Rodriguez, L.; Romero, C.; Schuster, K.; Sievers, A.; Triqueneaux, S.; Tucker, C.; Zylka, R.
2017-01-01
The determination of the thermodynamic properties of clusters of galaxies at intermediate and high redshift can bring new insights into the formation of large-scale structures. It is essential for a robust calibration of the mass-observable scaling relations and their scatter, which are key ingredients for precise cosmology using cluster statistics. Here we illustrate an application of high resolution (R 0.02 R500) to its outskirts (R 3 R500) non-parametrically for the first time at intermediate redshift. The constraints on the resulting pressure profile allow us to reduce the relative uncertainty on the integrated Compton parameter by a factor of two compared to the Planck value. Combining the tSZ data and the deprojected electronic density profile from XMM-Newton allows us to undertake a hydrostatic mass analysis, for which we study the impact of a spherical model assumption on the total mass estimate. We also investigate the radial temperature and entropy distributions. These data indicate that PSZ1 G045.85+57.71 is a massive (M500 5.5 × 1014M⊙) cool-core cluster. This work is part of a pilot study aiming at optimizing the treatment of the NIKA2 tSZ large program dedicated to the follow-up of SZ-discovered clusters at intermediate and high redshifts. This study illustrates the potential of NIKA2 to put constraints on thethermodynamic properties and tSZ-scaling relations of these clusters, and demonstrates the excellent synergy between tSZ and X-ray observations of similar angular resolution.
Wind speed forecasting at different time scales: a non parametric approach
D'Amico, Guglielmo; Prattico, Flavio
2013-01-01
The prediction of wind speed is one of the most important aspects when dealing with renewable energy. In this paper we show a new nonparametric model, based on semi-Markov chains, to predict wind speed. Particularly we use an indexed semi-Markov model, that reproduces accurately the statistical behavior of wind speed, to forecast wind speed one step ahead for different time scales and for very long time horizon maintaining the goodness of prediction. In order to check the main features of the model we show, as indicator of goodness, the root mean square error between real data and predicted ones and we compare our forecasting results with those of a persistence model.
A new measure for gene expression biclustering based on non-parametric correlation.
Flores, Jose L; Inza, Iñaki; Larrañaga, Pedro; Calvo, Borja
2013-12-01
One of the emerging techniques for performing the analysis of the DNA microarray data known as biclustering is the search of subsets of genes and conditions which are coherently expressed. These subgroups provide clues about the main biological processes. Until now, different approaches to this problem have been proposed. Most of them use the mean squared residue as quality measure but relevant and interesting patterns can not be detected such as shifting, or scaling patterns. Furthermore, recent papers show that there exist new coherence patterns involved in different kinds of cancer and tumors such as inverse relationships between genes which can not be captured. The proposed measure is called Spearman's biclustering measure (SBM) which performs an estimation of the quality of a bicluster based on the non-linear correlation among genes and conditions simultaneously. The search of biclusters is performed by using a evolutionary technique called estimation of distribution algorithms which uses the SBM measure as fitness function. This approach has been examined from different points of view by using artificial and real microarrays. The assessment process has involved the use of quality indexes, a set of bicluster patterns of reference including new patterns and a set of statistical tests. It has been also examined the performance using real microarrays and comparing to different algorithmic approaches such as Bimax, CC, OPSM, Plaid and xMotifs. SBM shows several advantages such as the ability to recognize more complex coherence patterns such as shifting, scaling and inversion and the capability to selectively marginalize genes and conditions depending on the statistical significance. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
Energy Technology Data Exchange (ETDEWEB)
Perlman, M D
1977-03-01
Research activities of the Department of Statistics, University of Chicago, during the period 15 June 1976 to 14 June 1977 are reviewed. Individual projects were carried out in the following eight areas: statistical computing--approximations to statistical tables and functions; numerical computation of boundary-crossing probabilities for Brownian motion and related stochastic processes; probabilistic methods in statistical mechanics; combining independent tests of significance; small-sample efficiencies of tests and estimates; improved procedures for simultaneous estimation and testing of many correlations; statistical computing and improved regression methods; and comparison of several populations. Brief summaries of these projects are given, along with other administrative information. (RWR)
Visualization methods for statistical analysis of microarray clusters
Directory of Open Access Journals (Sweden)
Li Kai
2005-05-01
Full Text Available Abstract Background The most common method of identifying groups of functionally related genes in microarray data is to apply a clustering algorithm. However, it is impossible to determine which clustering algorithm is most appropriate to apply, and it is difficult to verify the results of any algorithm due to the lack of a gold-standard. Appropriate data visualization tools can aid this analysis process, but existing visualization methods do not specifically address this issue. Results We present several visualization techniques that incorporate meaningful statistics that are noise-robust for the purpose of analyzing the results of clustering algorithms on microarray data. This includes a rank-based visualization method that is more robust to noise, a difference display method to aid assessments of cluster quality and detection of outliers, and a projection of high dimensional data into a three dimensional space in order to examine relationships between clusters. Our methods are interactive and are dynamically linked together for comprehensive analysis. Further, our approach applies to both protein and gene expression microarrays, and our architecture is scalable for use on both desktop/laptop screens and large-scale display devices. This methodology is implemented in GeneVAnD (Genomic Visual ANalysis of Datasets and is available at http://function.princeton.edu/GeneVAnD. Conclusion Incorporating relevant statistical information into data visualizations is key for analysis of large biological datasets, particularly because of high levels of noise and the lack of a gold-standard for comparisons. We developed several new visualization techniques and demonstrated their effectiveness for evaluating cluster quality and relationships between clusters.
A method for statistically comparing spatial distribution maps
Directory of Open Access Journals (Sweden)
Reynolds Mary G
2009-01-01
Full Text Available Abstract Background Ecological niche modeling is a method for estimation of species distributions based on certain ecological parameters. Thus far, empirical determination of significant differences between independently generated distribution maps for a single species (maps which are created through equivalent processes, but with different ecological input parameters, has been challenging. Results We describe a method for comparing model outcomes, which allows a statistical evaluation of whether the strength of prediction and breadth of predicted areas is measurably different between projected distributions. To create ecological niche models for statistical comparison, we utilized GARP (Genetic Algorithm for Rule-Set Production software to generate ecological niche models of human monkeypox in Africa. We created several models, keeping constant the case location input records for each model but varying the ecological input data. In order to assess the relative importance of each ecological parameter included in the development of the individual predicted distributions, we performed pixel-to-pixel comparisons between model outcomes and calculated the mean difference in pixel scores. We used a two sample Student's t-test, (assuming as null hypothesis that both maps were identical to each other regardless of which input parameters were used to examine whether the mean difference in corresponding pixel scores from one map to another was greater than would be expected by chance alone. We also utilized weighted kappa statistics, frequency distributions, and percent difference to look at the disparities in pixel scores. Multiple independent statistical tests indicated precipitation as the single most important independent ecological parameter in the niche model for human monkeypox disease. Conclusion In addition to improving our understanding of the natural factors influencing the distribution of human monkeypox disease, such pixel-to-pixel comparison
Non-Parametric, Closed-Loop Testing of Autonomy in Unmanned Aircraft Systems Project
National Aeronautics and Space Administration — The proposed Phase I program aims to develop new methods to support safety testing for integration of Unmanned Aircraft Systems into the National Airspace (NAS) with...
FOREWORD: Special issue on Statistical and Probabilistic Methods for Metrology
Bich, Walter; Cox, Maurice G.
2006-08-01
This special issue of Metrologia is the first that is not devoted to units, or constants, or measurement techniques in some specific field of metrology, but to the generic topic of statistical and probabilistic methods for metrology. The number of papers on this subject in measurement journals, and in Metrologia in particular, has continued to increase over the years, driven by the publication of the Guide to the Expression of Uncertainty in Measurement (GUM) [1] and the Mutual Recognition Arrangement (MRA) of the CIPM [2]. The former stimulated metrologists to think in greater depth about the appropriate modelling of their measurements, in order to provide uncertainty evaluations associated with measurement results. The latter obliged the metrological community to investigate reliable measures for assessing the calibration and measurement capabilities declared by the national metrology institutes (NMIs). Furthermore, statistical analysis of measurement data became even more important than hitherto, with the need, on the one hand, to treat the greater quantities of data provided by sophisticated measurement systems, and, on the other, to deal appropriately with relatively small sets of data that are difficult or expensive to obtain. The importance of supporting the GUM and extending its provisions was recognized by the formation in the year 2000 of Working Group 1, Measurement uncertainty, of the Joint Committee for Guides in Metrology. The need to provide guidance on key comparison data evaluation was recognized by the formation in the year 2001 of the BIPM Director's Advisory Group on Uncertainty. A further international initiative was the revision, in the year 2004, of the remit and title of a working group of ISO/TC 69, Application of Statistical Methods, to reflect the need to concentrate more on statistical methods to support measurement uncertainty evaluation. These international activities are supplemented by national programmes such as the Software Support
Hybrid Perturbation methods based on Statistical Time Series models
San-Juan, Juan Félix; Pérez, Iván; López, Rosario
2016-01-01
In this work we present a new methodology for orbit propagation, the hybrid perturbation theory, based on the combination of an integration method and a prediction technique. The former, which can be a numerical, analytical or semianalytical theory, generates an initial approximation that contains some inaccuracies derived from the fact that, in order to simplify the expressions and subsequent computations, not all the involved forces are taken into account and only low-order terms are considered, not to mention the fact that mathematical models of perturbations not always reproduce physical phenomena with absolute precision. The prediction technique, which can be based on either statistical time series models or computational intelligence methods, is aimed at modelling and reproducing missing dynamics in the previously integrated approximation. This combination results in the precision improvement of conventional numerical, analytical and semianalytical theories for determining the position and velocity of a...
Nonlinear diffusion methods based on robust statistics for noise removal
Institute of Scientific and Technical Information of China (English)
JIA Di-ye; HUANG Feng-gang; SU Han
2007-01-01
A novel smoothness term of Bayesian regularization framework based on M-estimation of robust statistics is proposed, and from this term a class of fourth-order nonlinear diffusion methods is proposed. These methods attempt to approximate an observed image with a piecewise linear image, which looks more natural than piecewise constant image used to approximate an observed image by P-M[1] model. It is known that M-estimators and W-estimators are essentially equivalent and solve the same minimization problem. Then, we propose PL bilateral filter from equivalent W-estimator. This new model is designed for piecewise linear image filtering,which is more effective than normal bilateral filter.
A test statistic for the affected-sib-set method.
Lange, K
1986-07-01
This paper discusses generalizations of the affected-sib-pair method. First, the requirement that sib identity-by-descent relations be known unambiguously is relaxed by substituting sib identity-by-state relations. This permits affected sibs to be used even when their parents are unavailable for typing. In the limit of an infinite number of marker alleles each of infinitesimal population frequency, the identity-by-state relations coincide with the usual identity-by-descent relations. Second, a weighted pairs test statistic is proposed that covers affected sib sets of size greater than two. These generalizations make the affected-sib-pair method a more powerful technique for detecting departures from independent segregation of disease and marker phenotypes. A sample calculation suggests such a departure for tuberculoid leprosy and the HLA D locus.
Statistical Inference Methods for Sparse Biological Time Series Data
Directory of Open Access Journals (Sweden)
Voit Eberhard O
2011-04-01
Full Text Available Abstract Background Comparing metabolic profiles under different biological perturbations has become a powerful approach to investigating the functioning of cells. The profiles can be taken as single snapshots of a system, but more information is gained if they are measured longitudinally over time. The results are short time series consisting of relatively sparse data that cannot be analyzed effectively with standard time series techniques, such as autocorrelation and frequency domain methods. In this work, we study longitudinal time series profiles of glucose consumption in the yeast Saccharomyces cerevisiae under different temperatures and preconditioning regimens, which we obtained with methods of in vivo nuclear magnetic resonance (NMR spectroscopy. For the statistical analysis we first fit several nonlinear mixed effect regression models to the longitudinal profiles and then used an ANOVA likelihood ratio method in order to test for significant differences between the profiles. Results The proposed methods are capable of distinguishing metabolic time trends resulting from different treatments and associate significance levels to these differences. Among several nonlinear mixed-effects regression models tested, a three-parameter logistic function represents the data with highest accuracy. ANOVA and likelihood ratio tests suggest that there are significant differences between the glucose consumption rate profiles for cells that had been--or had not been--preconditioned by heat during growth. Furthermore, pair-wise t-tests reveal significant differences in the longitudinal profiles for glucose consumption rates between optimal conditions and heat stress, optimal and recovery conditions, and heat stress and recovery conditions (p-values Conclusion We have developed a nonlinear mixed effects model that is appropriate for the analysis of sparse metabolic and physiological time profiles. The model permits sound statistical inference procedures
The binned bispectrum estimator: template-based and non-parametric CMB non-Gaussianity searches
Bucher, Martin; van Tent, Bartjan
2015-01-01
We describe the details of the binned bispectrum estimator as used for the official 2013 and 2015 analyses of the temperature and polarization CMB maps from the ESA Planck satellite. The defining aspect of this estimator is the determination of a map bispectrum (3-point correlator) that has been binned in harmonic space. For a parametric determination of the non-Gaussianity in the map (the so-called fNL parameters), one takes the inner product of this binned bispectrum with theoretically motivated templates. However, as a complementary approach one can also smooth the binned bispectrum using a variable smoothing scale in order to suppress noise and make coherent features stand out above the noise. This allows one to look in a model-independent way for any statistically significant bispectral signal. This approach is useful for characterizing the bispectral shape of the galactic foreground emission, for which a theoretical prediction of the bispectral anisotropy is lacking, and for detecting a serendipitous pr...
Directory of Open Access Journals (Sweden)
Jose H. Guardiola
2010-01-01
Full Text Available This paper compares the academic performance of students in three similar elementary statistics courses taught by the same instructor, but with the lab component differing among the three. One course is traditionally taught without a lab component; the second with a lab component using scenarios and an extensive use of technology, but without explicit coordination between lab and lecture; and the third using a lab component with an extensive use of technology that carefully coordinates the lab with the lecture. Extensive use of technology means, in this context, using Minitab software in the lab section, doing homework and quizzes using MyMathlab ©, and emphasizing interpretation of computer output during lectures. Initially, an online instrument based on Gardner’s multiple intelligences theory, is given to students to try to identify students’ learning styles and intelligence types as covariates. An analysis of covariance is performed in order to compare differences in achievement. In this study there is no attempt to measure difference in student performance across the different treatments. The purpose of this study is to find indications of associations among variables that support the claim that statistics labs could be associated with superior academic achievement in one of these three instructional environments. Also, this study tries to identify individual student characteristics that could be associated with superior academic performance. This study did not find evidence of any individual student characteristics that could be associated with superior achievement. The response variable was computed as percentage of correct answers for the three exams during the semester added together. The results of this study indicate a significant difference across these three different instructional methods, showing significantly higher mean scores for the response variable on students taking the lab component that was carefully coordinated with
Economic capacity estimation in fisheries: A non-parametric ray approach
Energy Technology Data Exchange (ETDEWEB)
Pascoe, Sean; Tingley, Diana [Centre for the Economics and Management of Aquatic Resources (CEMARE), University of Portsmouth, Boathouse No. 6, College Road, HM Naval Base, Portsmouth PO1 3LJ (United Kingdom)
2006-05-15
Data envelopment analysis (DEA) has generally been adopted as the most appropriate methodology for the estimation of fishing capacity, particularly in multi-species fisheries. More recently, economic DEA methods have been developed that incorporate the costs and benefits of increasing capacity utilisation. One such method was applied to estimate the capacity utilisation and output of the Scottish fleet. By comparing the results of the economic and traditional DEA approaches, it can be concluded that many fleet segments are operating at or close to full capacity, and that the vessels defining the frontier are operating consistent with profit maximising behaviour. (author)
A Probabilistic, Non-parametric Framework for Inter-modality Label Fusion
DEFF Research Database (Denmark)
Iglesias, Juan Eugenio; Sabuncu, Mert Rory; Van Leemput, Koen
2013-01-01
in a principled way in inter-modality scenarios remains an open problem. Here we propose a label fusion scheme that does not require voxel intensity consistency between the atlases and the target image to segment. The method is based on a generative model of image data in which each intensity in the atlases has...... an associated conditional distribution of corresponding intensities in the target. The segmentation is computed using variational expectation maximization (VEM) in a Bayesian framework. The method was evaluated with a dataset of eight proton density weighted brain MRI scans with nine labeled structures......Multi-atlas techniques are commonplace in medical image segmentation due to their high performance and ease of implementation. Locally weighting the contributions from the different atlases in the label fusion process can improve the quality of the segmentation. However, how to define these weights...
Comparison of prediction performance using statistical postprocessing methods
Han, Keunhee; Choi, JunTae; Kim, Chansoo
2016-11-01
As the 2018 Winter Olympics are to be held in Pyeongchang, both general weather information on Pyeongchang and specific weather information on this region, which can affect game operation and athletic performance, are required. An ensemble prediction system has been applied to provide more accurate weather information, but it has bias and dispersion due to the limitations and uncertainty of its model. In this study, homogeneous and nonhomogeneous regression models as well as Bayesian model averaging (BMA) were used to reduce the bias and dispersion existing in ensemble prediction and to provide probabilistic forecast. Prior to applying the prediction methods, reliability of the ensemble forecasts was tested by using a rank histogram and a residualquantile-quantile plot to identify the ensemble forecasts and the corresponding verifications. The ensemble forecasts had a consistent positive bias, indicating over-forecasting, and were under-dispersed. To correct such biases, statistical post-processing methods were applied using fixed and sliding windows. The prediction skills of methods were compared by using the mean absolute error, root mean square error, continuous ranked probability score, and continuous ranked probability skill score. Under the fixed window, BMA exhibited better prediction skill than the other methods in most observation station. Under the sliding window, on the other hand, homogeneous and non-homogeneous regression models with positive regression coefficients exhibited better prediction skill than BMA. In particular, the homogeneous regression model with positive regression coefficients exhibited the best prediction skill.
Liang, Rong; Zhou, Shu-dong; Li, Li-xia; Zhang, Jun-guo; Gao, Yan-hui
2013-09-01
This paper aims to achieve Bootstraping in hierarchical data and to provide a method for the estimation on confidence interval(CI) of intraclass correlation coefficient(ICC).First, we utilize the mixed-effects model to estimate data from ICC of repeated measurement and from the two-stage sampling. Then, we use Bootstrap method to estimate CI from related ICCs. Finally, the influences of different Bootstraping strategies to ICC's CIs are compared. The repeated measurement instance show that the CI of cluster Bootsraping containing the true ICC value. However, when ignoring the hierarchy characteristics of data, the random Bootsraping method shows that it has the invalid CI. Result from the two-stage instance shows that bias observed between cluster Bootstraping's ICC means while the ICC of the original sample is the smallest, but with wide CI. It is necessary to consider the structure of data as important, when hierarchical data is being resampled. Bootstrapping seems to be better on the higher than that on lower levels.
Statistical methods for the detection and analysis of radioactive sources
Klumpp, John
We consider four topics from areas of radioactive statistical analysis in the present study: Bayesian methods for the analysis of count rate data, analysis of energy data, a model for non-constant background count rate distributions, and a zero-inflated model of the sample count rate. The study begins with a review of Bayesian statistics and techniques for analyzing count rate data. Next, we consider a novel system for incorporating energy information into count rate measurements which searches for elevated count rates in multiple energy regions simultaneously. The system analyzes time-interval data in real time to sequentially update a probability distribution for the sample count rate. We then consider a "moving target" model of background radiation in which the instantaneous background count rate is a function of time, rather than being fixed. Unlike the sequential update system, this model assumes a large body of pre-existing data which can be analyzed retrospectively. Finally, we propose a novel Bayesian technique which allows for simultaneous source detection and count rate analysis. This technique is fully compatible with, but independent of, the sequential update system and moving target model.
A Statistical Method to Distinguish Functional Brain Networks
Fujita, André; Vidal, Maciel C.; Takahashi, Daniel Y.
2017-01-01
One major problem in neuroscience is the comparison of functional brain networks of different populations, e.g., distinguishing the networks of controls and patients. Traditional algorithms are based on search for isomorphism between networks, assuming that they are deterministic. However, biological networks present randomness that cannot be well modeled by those algorithms. For instance, functional brain networks of distinct subjects of the same population can be different due to individual characteristics. Moreover, networks of subjects from different populations can be generated through the same stochastic process. Thus, a better hypothesis is that networks are generated by random processes. In this case, subjects from the same group are samples from the same random process, whereas subjects from different groups are generated by distinct processes. Using this idea, we developed a statistical test called ANOGVA to test whether two or more populations of graphs are generated by the same random graph model. Our simulations' results demonstrate that we can precisely control the rate of false positives and that the test is powerful to discriminate random graphs generated by different models and parameters. The method also showed to be robust for unbalanced data. As an example, we applied ANOGVA to an fMRI dataset composed of controls and patients diagnosed with autism or Asperger. ANOGVA identified the cerebellar functional sub-network as statistically different between controls and autism (p < 0.001). PMID:28261045
Comparison of Three Statistical Classification Techniques for Maser Identification
Manning, Ellen M.; Holland, Barbara R.; Ellingsen, Simon P.; Breen, Shari L.; Chen, Xi; Humphries, Melissa
2016-04-01
We applied three statistical classification techniques-linear discriminant analysis (LDA), logistic regression, and random forests-to three astronomical datasets associated with searches for interstellar masers. We compared the performance of these methods in identifying whether specific mid-infrared or millimetre continuum sources are likely to have associated interstellar masers. We also discuss the interpretability of the results of each classification technique. Non-parametric methods have the potential to make accurate predictions when there are complex relationships between critical parameters. We found that for the small datasets the parametric methods logistic regression and LDA performed best, for the largest dataset the non-parametric method of random forests performed with comparable accuracy to parametric techniques, rather than any significant improvement. This suggests that at least for the specific examples investigated here accuracy of the predictions obtained is not being limited by the use of parametric models. We also found that for LDA, transformation of the data to match a normal distribution led to a significant improvement in accuracy. The different classification techniques had significant overlap in their predictions; further astronomical observations will enable the accuracy of these predictions to be tested.
An Non-parametrical Approach to Estimate Location Parameters under Simple Order
Institute of Scientific and Technical Information of China (English)
孙旭
2005-01-01
This paper deals with estimating parameters under simple order when samples come from location models. Based on the idea of Hodges and Lehmann estimator (H-L estimator), a new approach to estimate parameters is proposed, which is difference with the classical L1 isotoaic regression and L2 isotonic regression. An algorithm to corupute estimators is given. Simulations by the Monte-Carlo method is applied to compare the likelihood functions with respect to L1 estimators and weighted isotonic H-L estimators.
Jet Noise Diagnostics Supporting Statistical Noise Prediction Methods
Bridges, James E.
2006-01-01
compared against measurements of mean and rms velocity statistics over a range of jet speeds and temperatures. Models for flow parameters used in the acoustic analogy, most notably the space-time correlations of velocity, have been compared against direct measurements, and modified to better fit the observed data. These measurements have been extremely challenging for hot, high speed jets, and represent a sizeable investment in instrumentation development. As an intermediate check that the analysis is predicting the physics intended, phased arrays have been employed to measure source distributions for a wide range of jet cases. And finally, careful far-field spectral directivity measurements have been taken for final validation of the prediction code. Examples of each of these experimental efforts will be presented. The main result of these efforts is a noise prediction code, named JeNo, which is in middevelopment. JeNo is able to consistently predict spectral directivity, including aft angle directivity, for subsonic cold jets of most geometries. Current development on JeNo is focused on extending its capability to hot jets, requiring inclusion of a previously neglected second source associated with thermal fluctuations. A secondary result of the intensive experimentation is the archiving of various flow statistics applicable to other acoustic analogies and to development of time-resolved prediction methods. These will be of lasting value as we look ahead at future challenges to the aeroacoustic experimentalist.
Development and testing of improved statistical wind power forecasting methods.
Energy Technology Data Exchange (ETDEWEB)
Mendes, J.; Bessa, R.J.; Keko, H.; Sumaili, J.; Miranda, V.; Ferreira, C.; Gama, J.; Botterud, A.; Zhou, Z.; Wang, J. (Decision and Information Sciences); (INESC Porto)
2011-12-06
(with spatial and/or temporal dependence). Statistical approaches to uncertainty forecasting basically consist of estimating the uncertainty based on observed forecasting errors. Quantile regression (QR) is currently a commonly used approach in uncertainty forecasting. In Chapter 3, we propose new statistical approaches to the uncertainty estimation problem by employing kernel density forecast (KDF) methods. We use two estimators in both offline and time-adaptive modes, namely, the Nadaraya-Watson (NW) and Quantilecopula (QC) estimators. We conduct detailed tests of the new approaches using QR as a benchmark. One of the major issues in wind power generation are sudden and large changes of wind power output over a short period of time, namely ramping events. In Chapter 4, we perform a comparative study of existing definitions and methodologies for ramp forecasting. We also introduce a new probabilistic method for ramp event detection. The method starts with a stochastic algorithm that generates wind power scenarios, which are passed through a high-pass filter for ramp detection and estimation of the likelihood of ramp events to happen. The report is organized as follows: Chapter 2 presents the results of the application of ITL training criteria to deterministic WPF; Chapter 3 reports the study on probabilistic WPF, including new contributions to wind power uncertainty forecasting; Chapter 4 presents a new method to predict and visualize ramp events, comparing it with state-of-the-art methodologies; Chapter 5 briefly summarizes the main findings and contributions of this report.
Axial electron channeling statistical method of site occupancy determination
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Multibeams dynamical theory of electron diffraction has been used to calculate the fast electron thickness-integrated probability density on Ti and Al sites in the γ-TiAl phase as a function of the incident electron beam orientation along \\[100\\], \\[110\\] and \\[011\\] zone axes, with the effect of absorption considered. Both of the calculation and experiments show that there are big differences in electron channeling effect for different zone axes or the same axis but with different orientations, so we should choose proper zone axis and suitable incident beam tilting angles when using the axial electron channeling statistical method to determine the site occupancies of impurities. It is suggested to calculate the channeling effect map before the experiments.
NEW METHOD FOR CALCULATION OF STATISTIC MISTAKE IN MARKETING INVESTIGATIONS
Directory of Open Access Journals (Sweden)
V. A. Koldachiov
2008-01-01
Full Text Available An idea of a new method is that while breaking-down analysis sample in some sub-samples there is a probability that an actual value for general body will be inside the interval between the highest and lowest average meaning of sub-sample is much higher of the probability that the given value will be beyond the limits of the indicated interval. In this case a size of the interval appears to be less than analogous parameter while making calculation with the help of the Stewdent formula.Thus, it is possible to reach high accuracy in results of marketing investigations while preserving analysis sample size or reducing the necessary size of analysis sample while preserving level of statistical mistake.
Statistical methods for determining the effect of mammography screening
DEFF Research Database (Denmark)
Lophaven, Søren
2016-01-01
In an overview of five randomised controlled trials from Sweden, a reduction of 29% was found in breast cancer mortality in women aged 50-69 at randomisation after a follow up of 5-13 years. Organised, population based, mammography service screening was introduced on the basis of these resultsin...... the municipality of Copenhagen in 1991, in the county of Fyn in 1993 and in the municipality of Frederiksberg in 1994, although reduced mortality in randomised controlled trials does not necessarily mean that screening also works in routine health care. In the rest of Denmark mammography screening was introdueed...... in 2007-2008. Women aged 50-69 were invited to screening every second year. Taking advantage of the registers of population and health, we present statistical methods for evaluating the effect of mammography screening on breast cancer mortality (Olsen et al. 2005, Njor et al. 2015 and Weedon-Fekjær etal...
Bayesian Analysis of Multiple Populations I: Statistical and Computational Methods
Stenning, D C; Robinson, E; van Dyk, D A; von Hippel, T; Sarajedini, A; Stein, N
2016-01-01
We develop a Bayesian model for globular clusters composed of multiple stellar populations, extending earlier statistical models for open clusters composed of simple (single) stellar populations (vanDyk et al. 2009, Stein et al. 2013). Specifically, we model globular clusters with two populations that differ in helium abundance. Our model assumes a hierarchical structuring of the parameters in which physical properties---age, metallicity, helium abundance, distance, absorption, and initial mass---are common to (i) the cluster as a whole or to (ii) individual populations within a cluster, or are unique to (iii) individual stars. An adaptive Markov chain Monte Carlo (MCMC) algorithm is devised for model fitting that greatly improves convergence relative to its precursor non-adaptive MCMC algorithm. Our model and computational tools are incorporated into an open-source software suite known as BASE-9. We use numerical studies to demonstrate that our method can recover parameters of two-population clusters, and al...
Statistical methods for determining the effect of mammography screening
DEFF Research Database (Denmark)
Lophaven, Søren
2016-01-01
In an overview of five randomised controlled trials from Sweden, a reduction of 29% was found in breast cancer mortality in women aged 50-69 at randomisation after a follow up of 5-13 years. Organised, population based, mammography service screening was introduced on the basis of these resultsin...... the municipality of Copenhagen in 1991, in the county of Fyn in 1993 and in the municipality of Frederiksberg in 1994, although reduced mortality in randomised controlled trials does not necessarily mean that screening also works in routine health care. In the rest of Denmark mammography screening was introdueed...... in 2007-2008. Women aged 50-69 were invited to screening every second year. Taking advantage of the registers of population and health, we present statistical methods for evaluating the effect of mammography screening on breast cancer mortality (Olsen et al. 2005, Njor et al. 2015 and Weedon-Fekjær etal...
Non-parametric PSF estimation from celestial transit solar images using blind deconvolution
Gonzalez, Adriana; Jacques, Laurent
2016-01-01
Context: Characterization of instrumental effects in astronomical imaging is important in order to extract accurate physical information from the observations. Optics are never perfect and the non-ideal path through the telescope is usually represented by the convolution of an ideal image with a Point Spread Function (PSF). Other sources of noise (read-out, Photon) also contaminate the image acquisition process. The problem of estimating both the PSF filter and a denoised image is called blind deconvolution and is ill-posed. Aims: We propose a blind deconvolution scheme that relies on image regularization. Contrarily to most methods presented in the literature, it does not assume a parametric model of the PSF and can thus be applied to any telescope. Methods: Our scheme uses a wavelet analysis image prior model and weak assumptions on the PSF filter's response. We use the observations from a celestial body transit where such object can be assumed to be a black disk. Such constraints limits the interchangeabil...
Verhoeven, P.S.
2009-01-01
Although Statistics is not a very popular course according to most students, a majority of students still take it, as it is mandatory at most Social Science departments. Therefore it takes special teacher’s skills to teach statistics. In order to do so it is essential for teachers to know what stude
Assessment Methods in Statistical Education An International Perspective
Bidgood, Penelope; Jolliffe, Flavia
2010-01-01
This book is a collaboration from leading figures in statistical education and is designed primarily for academic audiences involved in teaching statistics and mathematics. The book is divided in four sections: (1) Assessment using real-world problems, (2) Assessment statistical thinking, (3) Individual assessment (4) Successful assessment strategies.
Directory of Open Access Journals (Sweden)
Shantanu Desai
2016-04-01
Full Text Available The coupling between spin and torsion in the Einstein–Cartan–Sciama–Kibble theory of gravity generates gravitational repulsion at very high densities, which prevents a singularity in a black hole and may create there a new universe. We show that quantum particle production in such a universe near the last bounce, which represents the Big Bang, gives the dynamics that solves the horizon, flatness, and homogeneity problems in cosmology. For a particular range of the particle production coefficient, we obtain a nearly constant Hubble parameter that gives an exponential expansion of the universe with more than 60 e-folds, which lasts about ∼10−42 s. This scenario can thus explain cosmic inflation without requiring a fundamental scalar field and reheating. From the obtained time dependence of the scale factor, we follow the prescription of Ellis and Madsen to reconstruct in a non-parametric way a scalar field potential which gives the same dynamics of the early universe. This potential gives the slow-roll parameters of cosmic inflation, from which we calculate the tensor-to-scalar ratio, the scalar spectral index of density perturbations, and its running as functions of the production coefficient. We find that these quantities do not significantly depend on the scale factor at the Big Bounce. Our predictions for these quantities are consistent with the Planck 2015 observations.
Desai, Shantanu; Popławski, Nikodem J.
2016-04-01
The coupling between spin and torsion in the Einstein-Cartan-Sciama-Kibble theory of gravity generates gravitational repulsion at very high densities, which prevents a singularity in a black hole and may create there a new universe. We show that quantum particle production in such a universe near the last bounce, which represents the Big Bang, gives the dynamics that solves the horizon, flatness, and homogeneity problems in cosmology. For a particular range of the particle production coefficient, we obtain a nearly constant Hubble parameter that gives an exponential expansion of the universe with more than 60 e-folds, which lasts about ∼10-42 s. This scenario can thus explain cosmic inflation without requiring a fundamental scalar field and reheating. From the obtained time dependence of the scale factor, we follow the prescription of Ellis and Madsen to reconstruct in a non-parametric way a scalar field potential which gives the same dynamics of the early universe. This potential gives the slow-roll parameters of cosmic inflation, from which we calculate the tensor-to-scalar ratio, the scalar spectral index of density perturbations, and its running as functions of the production coefficient. We find that these quantities do not significantly depend on the scale factor at the Big Bounce. Our predictions for these quantities are consistent with the Planck 2015 observations.
Desai, Shantanu
2015-01-01
The coupling between spin and torsion in the Einstein-Cartan-Sciama-Kibble theory of gravity generates gravitational repulsion at very high densities, which prevents a singularity in a black hole and may create there a new universe. We show that quantum particle production in such a universe near the last bounce, which represents the Big Bang gives the dynamics that solves the horizon, flatness, and homogeneity problems in cosmology. For a particular range of the particle production coefficient, we obtain a nearly constant Hubble parameter that gives an exponential expansion of the universe with more than 60 $e$-folds, which lasts about $\\sim 10^{-42}$ s. This scenario can thus explain cosmic inflation without requiring a fundamental scalar field and reheating. From the obtained time dependence of the scale factor, we follow the prescription of Ellis and Madsen to reconstruct in a non-parametric way a scalar field potential which gives the same dynamics of the early universe. This potential gives the slow-rol...
Erkal, Denis; Belokurov, Vasily
2016-01-01
Only in the Milky Way is it possible to conduct an experiment which uses stellar streams to detect low-mass dark matter subhaloes. In smooth and static host potentials, tidal tails of disrupting satellites appear highly symmetric. However, dark perturbers induce density fluctuations that destroy this symmetry. Motivated by the recent release of unprecedentedly deep and wide imaging data around the Pal 5 stellar stream, we develop a new probabilistic, adaptive and non-parametric technique which allows us to bring the cluster's tidal tails into clear focus. Strikingly, we uncover a stream whose density exhibits visible changes on a variety of angular scales. We detect significant bumps and dips, both narrow and broad: two peaks on either side of the progenitor, each only a fraction of a degree across, and two gaps, $\\sim2^{\\circ}$ and $\\sim9^{\\circ}$ wide, the latter accompanied by a gargantuan lump of debris. This largest density feature results in a pronounced inter-tail asymmetry which cannot be made consist...
Cotini, Stefano; Caccianiga, Alessandro; Colpi, Monica; Della Ceca, Roberto; Mapelli, Michela; Severgnini, Paola; Segreto, Alberto; 10.1093/mnras/stt358
2013-01-01
We investigate the possible link between mergers and the enhanced activity of supermassive black holes (SMBHs) at the centre of galaxies, by comparing the merger fraction of a local sample (0.003 =< z < 0.03) of active galaxies - 59 active galactic nuclei (AGN) host galaxies selected from the all-sky Swift BAT (Burst Alert Telescope) survey - with an appropriate control sample (247 sources extracted from the Hyperleda catalogue) that has the same redshift distribution as the BAT sample. We detect the interacting systems in the two samples on the basis of non-parametric structural indexes of concentration (C), asymmetry (A), clumpiness (S), Gini coefficient (G) and second order momentum of light (M20). In particular, we propose a new morphological criterion, based on a combination of all these indexes, that improves the identification of interacting systems. We also present a new software - PyCASSo (Python CAS Software) - for the automatic computation of the structural indexes. After correcting for the c...
Mayr, Andreas; Hothorn, Torsten; Fenske, Nora
2012-01-25
The construction of prediction intervals (PIs) for future body mass index (BMI) values of individual children based on a recent German birth cohort study with n = 2007 children is problematic for standard parametric approaches, as the BMI distribution in childhood is typically skewed depending on age. We avoid distributional assumptions by directly modelling the borders of PIs by additive quantile regression, estimated by boosting. We point out the concept of conditional coverage to prove the accuracy of PIs. As conditional coverage can hardly be evaluated in practical applications, we conduct a simulation study before fitting child- and covariate-specific PIs for future BMI values and BMI patterns for the present data. The results of our simulation study suggest that PIs fitted by quantile boosting cover future observations with the predefined coverage probability and outperform the benchmark approach. For the prediction of future BMI values, quantile boosting automatically selects informative covariates and adapts to the age-specific skewness of the BMI distribution. The lengths of the estimated PIs are child-specific and increase, as expected, with the age of the child. Quantile boosting is a promising approach to construct PIs with correct conditional coverage in a non-parametric way. It is in particular suitable for the prediction of BMI patterns depending on covariates, since it provides an interpretable predictor structure, inherent variable selection properties and can even account for longitudinal data structures.
Modular autopilot design and development featuring Bayesian non-parametric adaptive control
Stockton, Jacob
Over the last few decades, Unmanned Aircraft Systems, or UAS, have become a critical part of the defense of our nation and the growth of the aerospace sector. UAS have a great potential for the agricultural industry, first response, and ecological monitoring. However, the wide range of applications require many mission-specific vehicle platforms. These platforms must operate reliably in a range of environments, and in presence of significant uncertainties. The accepted practice for enabling autonomously flying UAS today relies on extensive manual tuning of the UAS autopilot parameters, or time consuming approximate modeling of the dynamics of the UAS. These methods may lead to overly conservative controllers or excessive development times. A comprehensive approach to the development of an adaptive, airframe-independent controller is presented. The control algorithm leverages a nonparametric, Bayesian approach to adaptation, and is used as a cornerstone for the development of a new modular autopilot. Promising simulation results are presented for the adaptive controller, as well as, flight test results for the modular autopilot.
Statistical analyses for NANOGrav 5-year timing residuals
Wang, Yan; Cordes, James M.; Jenet, Fredrick A.; Chatterjee, Shami; Demorest, Paul B.; Dolch, Timothy; Ellis, Justin A.; Lam, Michael T.; Madison, Dustin R.; McLaughlin, Maura A.; Perrodin, Delphine; Rankin, Joanna; Siemens, Xavier; Vallisneri, Michele
2017-02-01
In pulsar timing, timing residuals are the differences between the observed times of arrival and predictions from the timing model. A comprehensive timing model will produce featureless residuals, which are presumably composed of dominating noise and weak physical effects excluded from the timing model (e.g. gravitational waves). In order to apply optimal statistical methods for detecting weak gravitational wave signals, we need to know the statistical properties of noise components in the residuals. In this paper we utilize a variety of non-parametric statistical tests to analyze the whiteness and Gaussianity of the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) 5-year timing data, which are obtained from Arecibo Observatory and Green Bank Telescope from 2005 to 2010. We find that most of the data are consistent with white noise; many data deviate from Gaussianity at different levels, nevertheless, removing outliers in some pulsars will mitigate the deviations.
Statistical Analyses for NANOGrav 5-year Timing Residuals
Wang, Y; Jenet, F A; Chatterjee, S; Demorest, P B; Dolch, T; Ellis, J A; Lam, M T; Madison, D R; McLaughlin, M; Perrodin, D; Rankin, J; Siemens, X; Vallisneri, M
2016-01-01
In pulsar timing, timing residuals are the differences between the observed times of arrival and the predictions from the timing model. A comprehensive timing model will produce featureless residuals, which are presumably composed of dominating noise and weak physical effects excluded from the timing model (e.g. gravitational waves). In order to apply the optimal statistical methods for detecting the weak gravitational wave signals, we need to know the statistical properties of the noise components in the residuals. In this paper we utilize a variety of non-parametric statistical tests to analyze the whiteness and Gaussianity of the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) 5-year timing data which are obtained from the Arecibo Observatory and the Green Bank Telescope from 2005 to 2010 (Demorest et al. 2013). We find that most of the data are consistent with white noise; Many data deviate from Gaussianity at different levels, nevertheless, removing outliers in some pulsars will m...
Directory of Open Access Journals (Sweden)
Orlov A. I.
2016-05-01
Full Text Available Fuzzy sets are the special form of objects of nonnumeric nature. Therefore, in the processing of the sample, the elements of which are fuzzy sets, a variety of methods for the analysis of statistical data of any nature can be used - the calculation of the average, non-parametric density estimators, construction of diagnostic rules, etc. We have told about the development of our work on the theory of fuzziness (1975 - 2015. In the first of our work on fuzzy sets (1975, the theory of random sets is regarded as a generalization of the theory of fuzzy sets. In non-fiction series "Mathematics. Cybernetics" (publishing house "Knowledge" in 1980 the first book by a Soviet author fuzzy sets is published - our brochure "Optimization problems and fuzzy variables". This book is essentially a "squeeze" our research of 70-ies, ie, the research on the theory of stability and in particular on the statistics of objects of non-numeric nature, with a bias in the methodology. The book includes the main results of the fuzzy theory and its note to the random set theory, as well as new results (first publication! of statistics of fuzzy sets. On the basis of further experience, you can expect that the theory of fuzzy sets will be more actively applied in organizational and economic modeling of industry management processes. We discuss the concept of the average value of a fuzzy set. We have considered a number of statements of problems of testing statistical hypotheses on fuzzy sets. We have also proposed and justified some algorithms for restore relationships between fuzzy variables; we have given the representation of various variants of fuzzy cluster analysis of data and variables and described some methods of collection and description of fuzzy data
Emperical Laws in Economics Uncovered Using Methods in Statistical Mechanics
Stanley, H. Eugene
2001-06-01
In recent years, statistical physicists and computational physicists have determined that physical systems which consist of a large number of interacting particles obey universal "scaling laws" that serve to demonstrate an intrinsic self-similarity operating in such systems. Further, the parameters appearing in these scaling laws appear to be largely independent of the microscopic details. Since economic systems also consist of a large number of interacting units, it is plausible that scaling theory can be usefully applied to economics. To test this possibility using realistic data sets, a number of scientists have begun analyzing economic data using methods of statistical physics [1]. We have found evidence for scaling (and data collapse), as well as universality, in various quantities, and these recent results will be reviewed in this talk--starting with the most recent study [2]. We also propose models that may lead to some insight into these phenomena. These results will be discussed, as well as the overall rationale for why one might expect scaling principles to hold for complex economic systems. This work on which this talk is based is supported by BP, and was carried out in collaboration with L. A. N. Amaral S. V. Buldyrev, D. Canning, P. Cizeau, X. Gabaix, P. Gopikrishnan, S. Havlin, Y. Lee, Y. Liu, R. N. Mantegna, K. Matia, M. Meyer, C.-K. Peng, V. Plerou, M. A. Salinger, and M. H. R. Stanley. [1.] See, e.g., R. N. Mantegna and H. E. Stanley, Introduction to Econophysics: Correlations & Complexity in Finance (Cambridge University Press, Cambridge, 1999). [2.] P. Gopikrishnan, B. Rosenow, V. Plerou, and H. E. Stanley, "Identifying Business Sectors from Stock Price Fluctuations," e-print cond-mat/0011145; V. Plerou, P. Gopikrishnan, L. A. N. Amaral, X. Gabaix, and H. E. Stanley, "Diffusion and Economic Fluctuations," Phys. Rev. E (Rapid Communications) 62, 3023-3026 (2000); P. Gopikrishnan, V. Plerou, X. Gabaix, and H. E. Stanley, "Statistical Properties of
Statistical methods for detecting periodic fragments in DNA sequence data
Directory of Open Access Journals (Sweden)
Ying Hua
2011-04-01
Full Text Available Abstract Background Period 10 dinucleotides are structurally and functionally validated factors that influence the ability of DNA to form nucleosomes, histone core octamers. Robust identification of periodic signals in DNA sequences is therefore required to understand nucleosome organisation in genomes. While various techniques for identifying periodic components in genomic sequences have been proposed or adopted, the requirements for such techniques have not been considered in detail and confirmatory testing for a priori specified periods has not been developed. Results We compared the estimation accuracy and suitability for confirmatory testing of autocorrelation, discrete Fourier transform (DFT, integer period discrete Fourier transform (IPDFT and a previously proposed Hybrid measure. A number of different statistical significance procedures were evaluated but a blockwise bootstrap proved superior. When applied to synthetic data whose period-10 signal had been eroded, or for which the signal was approximately period-10, the Hybrid technique exhibited superior properties during exploratory period estimation. In contrast, confirmatory testing using the blockwise bootstrap procedure identified IPDFT as having the greatest statistical power. These properties were validated on yeast sequences defined from a ChIP-chip study where the Hybrid metric confirmed the expected dominance of period-10 in nucleosome associated DNA but IPDFT identified more significant occurrences of period-10. Application to the whole genomes of yeast and mouse identified ~ 21% and ~ 19% respectively of these genomes as spanned by period-10 nucleosome positioning sequences (NPS. Conclusions For estimating the dominant period, we find the Hybrid period estimation method empirically to be the most effective for both eroded and approximate periodicity. The blockwise bootstrap was found to be effective as a significance measure, performing particularly well in the problem of
System Synthesis in Preliminary Aircraft Design Using Statistical Methods
DeLaurentis, Daniel; Mavris, Dimitri N.; Schrage, Daniel P.
1996-01-01
This paper documents an approach to conceptual and early preliminary aircraft design in which system synthesis is achieved using statistical methods, specifically Design of Experiments (DOE) and Response Surface Methodology (RSM). These methods are employed in order to more efficiently search the design space for optimum configurations. In particular, a methodology incorporating three uses of these techniques is presented. First, response surface equations are formed which represent aerodynamic analyses, in the form of regression polynomials, which are more sophisticated than generally available in early design stages. Next, a regression equation for an Overall Evaluation Criterion is constructed for the purpose of constrained optimization at the system level. This optimization, though achieved in an innovative way, is still traditional in that it is a point design solution. The methodology put forward here remedies this by introducing uncertainty into the problem, resulting in solutions which are probabilistic in nature. DOE/RSM is used for the third time in this setting. The process is demonstrated through a detailed aero-propulsion optimization of a High Speed Civil Transport. Fundamental goals of the methodology, then, are to introduce higher fidelity disciplinary analyses to the conceptual aircraft synthesis and provide a roadmap for transitioning from point solutions to probabilistic designs (and eventually robust ones).
Hydrologic extremes - an intercomparison of multiple gridded statistical downscaling methods
Werner, Arelia T.; Cannon, Alex J.
2016-04-01
Gridded statistical downscaling methods are the main means of preparing climate model data to drive distributed hydrological models. Past work on the validation of climate downscaling methods has focused on temperature and precipitation, with less attention paid to the ultimate outputs from hydrological models. Also, as attention shifts towards projections of extreme events, downscaling comparisons now commonly assess methods in terms of climate extremes, but hydrologic extremes are less well explored. Here, we test the ability of gridded downscaling models to replicate historical properties of climate and hydrologic extremes, as measured in terms of temporal sequencing (i.e. correlation tests) and distributional properties (i.e. tests for equality of probability distributions). Outputs from seven downscaling methods - bias correction constructed analogues (BCCA), double BCCA (DBCCA), BCCA with quantile mapping reordering (BCCAQ), bias correction spatial disaggregation (BCSD), BCSD using minimum/maximum temperature (BCSDX), the climate imprint delta method (CI), and bias corrected CI (BCCI) - are used to drive the Variable Infiltration Capacity (VIC) model over the snow-dominated Peace River basin, British Columbia. Outputs are tested using split-sample validation on 26 climate extremes indices (ClimDEX) and two hydrologic extremes indices (3-day peak flow and 7-day peak flow). To characterize observational uncertainty, four atmospheric reanalyses are used as climate model surrogates and two gridded observational data sets are used as downscaling target data. The skill of the downscaling methods generally depended on reanalysis and gridded observational data set. However, CI failed to reproduce the distribution and BCSD and BCSDX the timing of winter 7-day low-flow events, regardless of reanalysis or observational data set. Overall, DBCCA passed the greatest number of tests for the ClimDEX indices, while BCCAQ, which is designed to more accurately resolve event
Improved statistical method for temperature and salinity quality control
Gourrion, Jérôme; Szekely, Tanguy
2017-04-01
Climate research and Ocean monitoring benefit from the continuous development of global in-situ hydrographic networks in the last decades. Apart from the increasing volume of observations available on a large range of temporal and spatial scales, a critical aspect concerns the ability to constantly improve the quality of the datasets. In the context of the Coriolis Dataset for ReAnalysis (CORA) version 4.2, a new quality control method based on a local comparison to historical extreme values ever observed is developed, implemented and validated. Temperature, salinity and potential density validity intervals are directly estimated from minimum and maximum values from an historical reference dataset, rather than from traditional mean and standard deviation estimates. Such an approach avoids strong statistical assumptions on the data distributions such as unimodality, absence of skewness and spatially homogeneous kurtosis. As a new feature, it also allows addressing simultaneously the two main objectives of an automatic quality control strategy, i.e. maximizing the number of good detections while minimizing the number of false alarms. The reference dataset is presently built from the fusion of 1) all ARGO profiles up to late 2015, 2) 3 historical CTD datasets and 3) the Sea Mammals CTD profiles from the MEOP database. All datasets are extensively and manually quality controlled. In this communication, the latest method validation results are also presented. The method has already been implemented in the latest version of the delayed-time CMEMS in-situ dataset and will be deployed soon in the equivalent near-real time products.
Directory of Open Access Journals (Sweden)
Derek Ruths
2008-02-01
Full Text Available Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods. Several researchers have observed that the connectivity of a network alone can provide significant insights into its dynamics. Motivated by this fundamental observation, we present the signaling Petri net, a non-parametric model of cellular signaling networks, and the signaling Petri net-based simulator, a Petri net execution strategy for characterizing the dynamics of signal flow through a signaling network using token distribution and sampling. The result is a very fast method, which can analyze large-scale networks, and provide insights into the trends of molecules' activity-levels in response to an external stimulus, based solely on the network's connectivity. We have implemented the signaling Petri net-based simulator in the PathwayOracle toolkit, which is publicly available at http
Determination of Reference Catalogs for Meridian Observations Using Statistical Method
Li, Z. Y.
2014-09-01
The meridian observational data are useful for developing high-precision planetary ephemerides of the solar system. These historical data are provided by the jet propulsion laboratory (JPL) or the Institut De Mecanique Celeste Et De Calcul Des Ephemerides (IMCCE). However, we find that the reference systems (realized by the fundamental catalogs FK3 (Third Fundamental Catalogue), FK4 (Fourth Fundamental Catalogue), and FK5 (Fifth Fundamental Catalogue), or Hipparcos), to which the observations are referred, are not given explicitly for some sets of data. The incompleteness of information prevents us from eliminating the systematic effects due to the different fundamental catalogs. The purpose of this paper is to specify clearly the reference catalogs of these observations with the problems in their records by using the JPL DE421 ephemeris. The data for the corresponding planets in the geocentric celestial reference system (GCRS) obtained from the DE421 are transformed to the apparent places with different hypothesis regarding the reference catalogs. Then the validations of the hypothesis are tested by two kinds of statistical quantities which are used to indicate the significance of difference between the original and transformed data series. As a result, this method is proved to be effective for specifying the reference catalogs, and the missed information is determined unambiguously. Finally these meridian data are transformed to the GCRS for further applications in the development of planetary ephemerides.
Energy Technology Data Exchange (ETDEWEB)
Wallace, D L; Perlman, M D
1980-06-01
This report describes the research activities of the Department of Statistics, University of Chicago, during the period June 15, 1975 to July 30, 1979. Nine research projects are briefly described on the following subjects: statistical computing and approximation techniques in statistics; numerical computation of first passage distributions; probabilities of large deviations; combining independent tests of significance; small-sample efficiencies of tests and estimates; improved procedures for simultaneous estimation and testing of many correlations; statistical computing and improved regression methods; comparison of several populations; and unbiasedness in multivariate statistics. A description of the statistical consultation activities of the Department that are of interest to DOE, in particular, the scientific interactions between the Department and the scientists at Argonne National Laboratories, is given. A list of publications issued during the term of the contract is included.
Statistical methods for decision making in mine action
DEFF Research Database (Denmark)
Larsen, Jan
The lecture discusses the basics of statistical decision making in connection with humanitarian mine action. There is special focus on: 1) requirements for mine detection; 2) design and evaluation of mine equipment; 3) performance improvement by statistical learning and information fusion; 4...
Statistics a guide to the use of statistical methods in the physical sciences
Barlow, Roger J
1989-01-01
The Manchester Physics Series General Editors: D. J. Sandiford; F. Mandl; A. C. Phillips Department of Physics and Astronomy, University of Manchester Properties of Matter B. H. Flowers and E. Mendoza Optics Second Edition F. G. Smith and J. H. Thomson Statistical Physics Second Edition F. Mandl Electromagnetism Second Edition I. S. Grant and W. R. Phillips Statistics R. J. Barlow Solid State Physics Second Edition J. R. Hook and H. E. Hall Quantum Mechanics F. Mandl Particle Physics Second Edition B. R. Martin and G. Shaw The Physics of Stars Second Edition A.C. Phillips Computing for Scienti
Erkal, Denis; Koposov, Sergey E.; Belokurov, Vasily
2017-09-01
Only in the Milky Way is it possible to conduct an experiment that uses stellar streams to detect low-mass dark matter subhaloes. In smooth and static host potentials, tidal tails of disrupting satellites appear highly symmetric. However, perturbations from dark subhaloes, as well as from GMCs and the Milky Way bar, can induce density fluctuations that destroy this symmetry. Motivated by the recent release of unprecedentedly deep and wide imaging data around the Pal 5 stellar stream, we develop a new probabilistic, adaptive and non-parametric technique that allows us to bring the cluster's tidal tails into clear focus. Strikingly, we uncover a stream whose density exhibits visible changes on a variety of angular scales. We detect significant bumps and dips, both narrow and broad: two peaks on either side of the progenitor, each only a fraction of a degree across, and two gaps, ∼2° and ∼9° wide, the latter accompanied by a gargantuan lump of debris. This largest density feature results in a pronounced intertail asymmetry which cannot be made consistent with an unperturbed stream according to a suite of simulations we have produced. We conjecture that the sharp peaks around Pal 5 are epicyclic overdensities, while the two dips are consistent with impacts by subhaloes. Assuming an age of 3.4 Gyr for Pal 5, these two gaps would correspond to the characteristic size of gaps created by subhaloes in the mass range of 106-107 M⊙ and 107-108 M⊙, respectively. In addition to dark substructure, we find that the bar of the Milky Way can plausibly produce the asymmetric density seen in Pal 5 and that GMCs could cause the smaller gap.
Mathematical statistics and stochastic processes
Bosq, Denis
2013-01-01
Generally, books on mathematical statistics are restricted to the case of independent identically distributed random variables. In this book however, both this case AND the case of dependent variables, i.e. statistics for discrete and continuous time processes, are studied. This second case is very important for today's practitioners.Mathematical Statistics and Stochastic Processes is based on decision theory and asymptotic statistics and contains up-to-date information on the relevant topics of theory of probability, estimation, confidence intervals, non-parametric statistics and rob
Mura, Maria Chiara; De Felice, Marco; Morlino, Roberta; Fuselli, Sergio
2010-01-01
In step with the need to develop statistical procedures to manage small-size environmental samples, in this work we have used concentration values of benzene (C6H6), concurrently detected by seven outdoor and indoor monitoring stations over 12 000 minutes, in order to assess the representativeness of collected data and the impact of the pollutant on indoor environment. Clearly, the former issue is strictly connected to sampling-site geometry, which proves critical to correctly retrieving information from analysis of pollutants of sanitary interest. Therefore, according to current criteria for network-planning, single stations have been interpreted as nodes of a set of adjoining triangles; then, a) node pairs have been taken into account in order to estimate pollutant stationarity on triangle sides, as well as b) node triplets, to statistically associate data from air-monitoring with the corresponding territory area, and c) node sextuplets, to assess the impact probability of the outdoor pollutant on indoor environment for each area. Distributions from the various node combinations are all non-Gaussian, in the consequently, Kruskal-Wallis (KW) non-parametric statistics has been exploited to test variability on continuous density function from each pair, triplet and sextuplet. Results from the above-mentioned statistical analysis have shown randomness of site selection, which has not allowed a reliable generalization of monitoring data to the entire selected territory, except for a single "forced" case (70%); most important, they suggest a possible procedure to optimize network design.
Directory of Open Access Journals (Sweden)
Maria Chiara Mura
2010-12-01
Full Text Available In step with the need to develop statistical procedures to manage small-size environmental samples, in this work we have used concentration values of benzene (C6H6, concurrently detected by seven outdoor and indoor monitoring stations over 12 000 minutes, in order to assess the representativeness of collected data and the impact of the pollutant on indoor environment. Clearly, the former issue is strictly connected to sampling-site geometry, which proves critical to correctly retrieving information from analysis of pollutants of sanitary interest. Therefore, according to current criteria for network-planning, single stations have been interpreted as nodes of a set of adjoining triangles; then, a node pairs have been taken into account in order to estimate pollutant stationarity on triangle sides, as well as b node triplets, to statistically associate data from air-monitoring with the corresponding territory area, and c node sextuplets, to assess the impact probability of the outdoor pollutant on indoor environment for each area. Distributions from the various node combinations are all non-Gaussian, in the consequently, Kruskal-Wallis (KW non-parametric statistics has been exploited to test variability on continuous density function from each pair, triplet and sextuplet. Results from the above-mentioned statistical analysis have shown randomness of site selection, which has not allowed a reliable generalization of monitoring data to the entire selected territory, except for a single "forced" case (70%; most important, they suggest a possible procedure to optimize network design.
Grootes, M W; Popescu, C C; Robotham, A S G; Seibert, M; Kelvin, L S
2013-01-01
(Abridged) We present a non-parametric cell-based method of selecting highly pure and largely complete samples of spiral galaxies using photometric and structural parameters as provided by standard photometric pipelines and simple shape fitting algorithms, demonstrably superior to commonly used proxies. Furthermore, we find structural parameters derived using passbands longwards of the $g$ band and linked to older stellar populations, especially the stellar mass surface density $\\mu_*$ and the $r$ band effective radius $r_e$, to perform at least equally well as parameters more traditionally linked to the identification of spirals by means of their young stellar populations. In particular the distinct bimodality in the parameter $\\mu_*$, consistent with expectations of different evolutionary paths for spirals and ellipticals, represents an often overlooked yet powerful parameter in differentiating between spiral and non-spiral/elliptical galaxies. We investigate the intrinsic specific star-formation rate - ste...
Robust Control Methods for On-Line Statistical Learning
Directory of Open Access Journals (Sweden)
Capobianco Enrico
2001-01-01
Full Text Available The issue of controlling that data processing in an experiment results not affected by the presence of outliers is relevant for statistical control and learning studies. Learning schemes should thus be tested for their capacity of handling outliers in the observed training set so to achieve reliable estimates with respect to the crucial bias and variance aspects. We describe possible ways of endowing neural networks with statistically robust properties by defining feasible error criteria. It is convenient to cast neural nets in state space representations and apply both Kalman filter and stochastic approximation procedures in order to suggest statistically robustified solutions for on-line learning.
Statistical methods in joint modeling of longitudinal and survival data
Dempsey, Walter
Survival studies often generate not only a survival time for each patient but also a sequence of health measurements at annual or semi-annual check-ups while the patient remains alive. Such a sequence of random length accompanied by a survival time is called a survival process. Ordinarily robust health is associated with longer survival, so the two parts of a survival process cannot be assumed independent. The first part of the thesis is concerned with a general technique---reverse alignment---for constructing statistical models for survival processes. A revival model is a regression model in the sense that it incorporates covariate and treatment effects into both the distribution of survival times and the joint distribution of health outcomes. The revival model also determines a conditional survival distribution given the observed history, which describes how the subsequent survival distribution is determined by the observed progression of health outcomes. The second part of the thesis explores the concept of a consistent exchangeable survival process---a joint distribution of survival times in which the risk set evolves as a continuous-time Markov process with homogeneous transition rates. A correspondence with the de Finetti approach of constructing an exchangeable survival process by generating iid survival times conditional on a completely independent hazard measure is shown. Several specific processes are detailed, showing how the number of blocks of tied failure times grows asymptotically with the number of individuals in each case. In particular, we show that the set of Markov survival processes with weakly continuous predictive distributions can be characterized by a two-dimensional family called the harmonic process. The outlined methods are then applied to data, showing how they can be easily extended to handle censoring and inhomogeneity among patients.
A comparative assessment of statistical methods for extreme weather analysis
Schlögl, Matthias; Laaha, Gregor
2017-04-01
Extreme weather exposure assessment is of major importance for scientists and practitioners alike. We compare different extreme value approaches and fitting methods with respect to their value for assessing extreme precipitation and temperature impacts. Based on an Austrian data set from 25 meteorological stations representing diverse meteorological conditions, we assess the added value of partial duration series over the standardly used annual maxima series in order to give recommendations for performing extreme value statistics of meteorological hazards. Results show the merits of the robust L-moment estimation, which yielded better results than maximum likelihood estimation in 62 % of all cases. At the same time, results question the general assumption of the threshold excess approach (employing partial duration series, PDS) being superior to the block maxima approach (employing annual maxima series, AMS) due to information gain. For low return periods (non-extreme events) the PDS approach tends to overestimate return levels as compared to the AMS approach, whereas an opposite behavior was found for high return levels (extreme events). In extreme cases, an inappropriate threshold was shown to lead to considerable biases that may outperform the possible gain of information from including additional extreme events by far. This effect was neither visible from the square-root criterion, nor from standardly used graphical diagnosis (mean residual life plot), but from a direct comparison of AMS and PDS in synoptic quantile plots. We therefore recommend performing AMS and PDS approaches simultaneously in order to select the best suited approach. This will make the analyses more robust, in cases where threshold selection and dependency introduces biases to the PDS approach, but also in cases where the AMS contains non-extreme events that may introduce similar biases. For assessing the performance of extreme events we recommend conditional performance measures that focus
Statistical Models and Methods for Network Meta-Analysis.
Madden, L V; Piepho, H-P; Paul, P A
2016-08-01
Meta-analysis, the methodology for analyzing the results from multiple independent studies, has grown tremendously in popularity over the last four decades. Although most meta-analyses involve a single effect size (summary result, such as a treatment difference) from each study, there are often multiple treatments of interest across the network of studies in the analysis. Multi-treatment (or network) meta-analysis can be used for simultaneously analyzing the results from all the treatments. However, the methodology is considerably more complicated than for the analysis of a single effect size, and there have not been adequate explanations of the approach for agricultural investigations. We review the methods and models for conducting a network meta-analysis based on frequentist statistical principles, and demonstrate the procedures using a published multi-treatment plant pathology data set. A major advantage of network meta-analysis is that correlations of estimated treatment effects are automatically taken into account when an appropriate model is used. Moreover, treatment comparisons may be possible in a network meta-analysis that are not possible in a single study because all treatments of interest may not be included in any given study. We review several models that consider the study effect as either fixed or random, and show how to interpret model-fitting output. We further show how to model the effect of moderator variables (study-level characteristics) on treatment effects, and present one approach to test for the consistency of treatment effects across the network. Online supplemental files give explanations on fitting the network meta-analytical models using SAS.
Understanding data better with Bayesian and global statistical methods
Press, W H
1996-01-01
To understand their data better, astronomers need to use statistical tools that are more advanced than traditional ``freshman lab'' statistics. As an illustration, the problem of combining apparently incompatible measurements of a quantity is presented from both the traditional, and a more sophisticated Bayesian, perspective. Explicit formulas are given for both treatments. Results are shown for the value of the Hubble Constant, and a 95% confidence interval of 66 < H0 < 82 (km/s/Mpc) is obtained.
Ludwig, Oswaldo
2012-01-01
Tese de doutoramento em Engenharia Eletrotécnica e de Computadores, no ramo de especialização em Automação e Robótica, apresentada à Faculdade de Ciências e Tecnologia da Universidade de Coimbra Esta tese concentra-se em reconhecimento de padrões, com particular ênfase para o con ito de escolha entre capacidade de generalização e custo computacional, a m de fornecer suporte para aplicações em tempo real. Neste contexto são apresentadas contribuições metodológicas e analíticas par...
Ludwig, Oswaldo
2012-01-01
Tese de doutoramento em Engenharia Eletrotécnica e de Computadores, no ramo de especialização em Automação e Robótica, apresentada à Faculdade de Ciências e Tecnologia da Universidade de Coimbra Esta tese concentra-se em reconhecimento de padrões, com particular ênfase para o con ito de escolha entre capacidade de generalização e custo computacional, a m de fornecer suporte para aplicações em tempo real. Neste contexto são apresentadas contribuições metodológicas e analíticas par...
Colon-Berlingeri, Migdalisel; Burrowes, Patricia A
2011-01-01
Incorporation of mathematics into biology curricula is critical to underscore for undergraduate students the relevance of mathematics to most fields of biology and the usefulness of developing quantitative process skills demanded in modern biology. At our institution, we have made significant changes to better integrate mathematics into the undergraduate biology curriculum. The curricular revision included changes in the suggested course sequence, addition of statistics and precalculus as prerequisites to core science courses, and incorporating interdisciplinary (math-biology) learning activities in genetics and zoology courses. In this article, we describe the activities developed for these two courses and the assessment tools used to measure the learning that took place with respect to biology and statistics. We distinguished the effectiveness of these learning opportunities in helping students improve their understanding of the math and statistical concepts addressed and, more importantly, their ability to apply them to solve a biological problem. We also identified areas that need emphasis in both biology and mathematics courses. In light of our observations, we recommend best practices that biology and mathematics academic departments can implement to train undergraduates for the demands of modern biology.
Ing, Alex; Schwarzbauer, Christian
2014-01-01
Functional connectivity has become an increasingly important area of research in recent years. At a typical spatial resolution, approximately 300 million connections link each voxel in the brain with every other. This pattern of connectivity is known as the functional connectome. Connectivity is often compared between experimental groups and conditions. Standard methods used to control the type 1 error rate are likely to be insensitive when comparisons are carried out across the whole connectome, due to the huge number of statistical tests involved. To address this problem, two new cluster based methods – the cluster size statistic (CSS) and cluster mass statistic (CMS) – are introduced to control the family wise error rate across all connectivity values. These methods operate within a statistical framework similar to the cluster based methods used in conventional task based fMRI. Both methods are data driven, permutation based and require minimal statistical assumptions. Here, the performance of each procedure is evaluated in a receiver operator characteristic (ROC) analysis, utilising a simulated dataset. The relative sensitivity of each method is also tested on real data: BOLD (blood oxygen level dependent) fMRI scans were carried out on twelve subjects under normal conditions and during the hypercapnic state (induced through the inhalation of 6% CO2 in 21% O2 and 73%N2). Both CSS and CMS detected significant changes in connectivity between normal and hypercapnic states. A family wise error correction carried out at the individual connection level exhibited no significant changes in connectivity. PMID:24906136
Ing, Alex; Schwarzbauer, Christian
2014-01-01
Functional connectivity has become an increasingly important area of research in recent years. At a typical spatial resolution, approximately 300 million connections link each voxel in the brain with every other. This pattern of connectivity is known as the functional connectome. Connectivity is often compared between experimental groups and conditions. Standard methods used to control the type 1 error rate are likely to be insensitive when comparisons are carried out across the whole connectome, due to the huge number of statistical tests involved. To address this problem, two new cluster based methods--the cluster size statistic (CSS) and cluster mass statistic (CMS)--are introduced to control the family wise error rate across all connectivity values. These methods operate within a statistical framework similar to the cluster based methods used in conventional task based fMRI. Both methods are data driven, permutation based and require minimal statistical assumptions. Here, the performance of each procedure is evaluated in a receiver operator characteristic (ROC) analysis, utilising a simulated dataset. The relative sensitivity of each method is also tested on real data: BOLD (blood oxygen level dependent) fMRI scans were carried out on twelve subjects under normal conditions and during the hypercapnic state (induced through the inhalation of 6% CO2 in 21% O2 and 73%N2). Both CSS and CMS detected significant changes in connectivity between normal and hypercapnic states. A family wise error correction carried out at the individual connection level exhibited no significant changes in connectivity.
Statistical methods and applications from a historical perspective selected issues
Mignani, Stefania
2014-01-01
The book showcases a selection of peer-reviewed papers, the preliminary versions of which were presented at a conference held 11-13 June 2011 in Bologna and organized jointly by the Italian Statistical Society (SIS), the National Institute of Statistics (ISTAT) and the Bank of Italy. The theme of the conference was "Statistics in the 150 years of the Unification of Italy." The celebration of the anniversary of Italian unification provided the opportunity to examine and discuss the methodological aspects and applications from a historical perspective and both from a national and international point of view. The critical discussion on the issues of the past has made it possible to focus on recent advances, considering the studies of socio-economic and demographic changes in European countries.
Barron, Kenneth E.; Apple, Kevin J.
2014-01-01
Coursework in statistics and research methods is a core requirement in most undergraduate psychology programs. However, is there an optimal way to structure and sequence methodology courses to facilitate student learning? For example, should statistics be required before research methods, should research methods be required before statistics, or…
Barron, Kenneth E.; Apple, Kevin J.
2014-01-01
Coursework in statistics and research methods is a core requirement in most undergraduate psychology programs. However, is there an optimal way to structure and sequence methodology courses to facilitate student learning? For example, should statistics be required before research methods, should research methods be required before statistics, or…
Critical Realism and Statistical Methods--A Response to Nash
Scott, David
2007-01-01
This article offers a defence of critical realism in the face of objections Nash (2005) makes to it in a recent edition of this journal. It is argued that critical and scientific realisms are closely related and that both are opposed to statistical positivism. However, the suggestion is made that scientific realism retains (from statistical…