Compositional ideas in the bayesian analysis of categorical data with application to dose finding clinical trials

Compositional random vectors are fundamental tools in the Bayesian analysis of categorical data. Many of the issues that are discussed with reference to the statistical analysis of compositional data have a natural counterpart in the construction of a Bayesian statistical model for categorical data. This note builds on the idea of cross-fertilization of the two areas recommended by Aitchison (1986) in his seminal book on compositional data. Particular emphasis is put on the problem of what parameterization to use

Indications for early hip revision surgery in the UK : A re-analysis of NJR data

This study determined the rate and indication for revision between cemented, uncemented, hybrid and resurfacing groups from NJR (6 th edition) data. Data validity was determined by interrogating for episodes of misclassification. We identified 6,034 (2.7%) misclassified episodes, containing 97 (4.3%) revisions. Kaplan-Meier revision rates at 3 years were 0.9% cemented, 1.9% for uncemented, 1.2% for hybrids and 3.0% for resurfacings (significant difference across all groups, p<0.001, with identical pattern in patients <55 years). Regression analysis indicated both prosthesis group and age significantly influenced failure (p<0.001). Revision for pain, aseptic loosening, and malalignment were highest in uncemented and resurfacing arthroplasty. Revision for dislocation was highest in uncemented hips (significant difference between groups, p<0.001). Feedback to the NJR on data misclassification has been made for future analysis. © 2012 Wichtig Editore.

A framework for qualitative analysis of focus group data in information systems

Focus groups are a popular qualitative research method for information systems researchers. However, compared with the abundance of research articles and handbooks on planning and conducting focus groups, surprisingly, there is little research on how to analyse focus group data. Moreover, those few articles that specifically address focus group analysis are all in fields other than information systems, and offer little specific guidance for information systems researchers. Further, even the studies that exist in other fields do not provide a systematic and integrated procedure to analyse both focus group ‘content’ and ‘interaction’ data. As the focus group is a valuable method to answer the research questions of many IS studies (in the business, government and society contexts), we believe that more attention should be paid to this method in the IS research. This paper offers a systematic and integrated procedure for qualitative focus group data analysis in information systems research.

Combining HPLC-DAD and ICP-MS data for improved analysis of complex samples: Classification of the root samples from Cortex moutan

A combined data matrix consisting of high performance liquid chromatography–diode array detector (HPLC–DAD) and inductively coupled plasma-mass spectrometry (ICP-MS) measurements of samples from the plant roots of the Cortex moutan (CM), produced much better classification and prediction results in comparison with those obtained from either of the individual data sets. The HPLC peaks (organic components) of the CM samples, and the ICP-MS measurements (trace metal elements) were investigated with the use of principal component analysis (PCA) and the linear discriminant analysis (LDA) methods of data analysis; essentially, qualitative results suggested that discrimination of the CM samples from three different provinces was possible with the combined matrix producing best results. Another three methods, K-nearest neighbor (KNN), back-propagation artificial neural network (BP-ANN) and least squares support vector machines (LS-SVM) were applied for the classification and prediction of the samples. Again, the combined data matrix analyzed by the KNN method produced best results (100% correct; prediction set data). Additionally, multiple linear regression (MLR) was utilized to explore any relationship between the organic constituents and the metal elements of the CM samples; the extracted linear regression equations showed that the essential metals as well as some metallic pollutants were related to the organic compounds on the basis of their concentrations

Missing in space: An evaluation of imputation methods for missing data in spatial analysis of risk factors for type II diabetes

Background Spatial analysis is increasingly important for identifying modifiable geographic risk factors for disease. However, spatial health data from surveys are often incomplete, ranging from missing data for only a few variables, to missing data for many variables. For spatial analyses of health outcomes, selection of an appropriate imputation method is critical in order to produce the most accurate inferences. Methods We present a cross-validation approach to select between three imputation methods for health survey data with correlated lifestyle covariates, using as a case study, type II diabetes mellitus (DM II) risk across 71 Queensland Local Government Areas (LGAs). We compare the accuracy of mean imputation to imputation using multivariate normal and conditional autoregressive prior distributions. Results Choice of imputation method depends upon the application and is not necessarily the most complex method. Mean imputation was selected as the most accurate method in this application. Conclusions Selecting an appropriate imputation method for health survey data, after accounting for spatial correlation and correlation between covariates, allows more complete analysis of geographic risk factors for disease with more confidence in the results to inform public policy decision-making.

Re-analysis of microarray data reveals insights into altered transcriptional activity of TH17 and TReg signalling in psoriasis

Identifying differential expression of genes in psoriatic and healthy skin by microarray data analysis is a key approach to understand the pathogenesis of psoriasis. Analysis of more than one dataset to identify genes commonly upregulated reduces the likelihood of false positives and narrows down the possible signature genes. Genes controlling the critical balance between T helper 17 and regulatory T cells are of special interest in psoriasis. Our objectives were to identify genes that are consistently upregulated in lesional skin from three published microarray datasets. We carried out a reanalysis of gene expression data extracted from three experiments on samples from psoriatic and nonlesional skin using the same stringency threshold and software and further compared the expression levels of 92 genes related to the T helper 17 and regulatory T cell signaling pathways. We found 73 probe sets representing 57 genes commonly upregulated in lesional skin from all datasets. These included 26 probe sets representing 20 genes that have no previous link to the etiopathogenesis of psoriasis. These genes may represent novel therapeutic targets and surely need more rigorous experimental testing to be validated. Our analysis also identified 12 of 92 genes known to be related to the T helper 17 and regulatory T cell signaling pathways, and these were found to be differentially expressed in the lesional skin samples.

An analysis of implied volatility jump dynamics: Novel functional data representation in crude oil markets

The predominant fear in capital markets is that of a price spike. Commodity markets differ in that there is a fear of both upward and down jumps, this results in implied volatility curves displaying distinct shapes when compared to equity markets. The use of a novel functional data analysis (FDA) approach, provides a framework to produce and interpret functional objects that characterise the underlying dynamics of oil future options. We use the FDA framework to examine implied volatility, jump risk, and pricing dynamics within crude oil markets. Examining a WTI crude oil sample for the 2007–2013 period, which includes the global financial crisis and the Arab Spring, strong evidence is found of converse jump dynamics during periods of demand and supply side weakness. This is used as a basis for an FDA-derived Merton (1976) jump diffusion optimised delta hedging strategy, which exhibits superior portfolio management results over traditional methods.

Some reflections on acquisition, processing and analysis of statistical data in forest soils

Beyond the classical statistical approaches (determination of basic statistics, regression analysis, ANOVA, etc.) a new set of applications of different statistical techniques has increasingly gained relevance in the analysis, processing and interpretation of data concerning the characteristics of forest soils. This is possible to be seen in some of the recent publications in the context of Multivariate Statistics. These new methods require additional care that is not always included or refered in some approaches. In the particular case of geostatistical data applications it is necessary, besides to geo-reference all the data acquisition, to collect the samples in regular grids and in sufficient quantity so that the variograms can reflect the spatial distribution of soil properties in a representative manner. In the case of the great majority of Multivariate Statistics techniques (Principal Component Analysis, Correspondence Analysis, Cluster Analysis, etc.) despite the fact they do not require in most cases the assumption of normal distribution, they however need a proper and rigorous strategy for its utilization. In this work, some reflections about these methodologies and, in particular, about the main constraints that often occur during the information collecting process and about the various linking possibilities of these different techniques will be presented. At the end, illustrations of some particular cases of the applications of these statistical methods will also be presented.

Analysis of Consumption Data to Detect Commercial Losses Using Performance Evaluation Methods in a Smart Grid

Electric power networks, namely distribution networks, have been suffering several changes during the last years due to changes in the power systems operation, towards the implementation of smart grids. Several approaches to the operation of the resources have been introduced, as the case of demand response, making use of the new capabilities of the smart grids. In the initial levels of the smart grids implementation reduced amounts of data are generated, namely consumption data. The methodology proposed in the present paper makes use of demand response consumers’ performance evaluation methods to determine the expected consumption for a given consumer. Then, potential commercial losses are identified using monthly historic consumption data. Real consumption data is used in the case study to demonstrate the application of the proposed method.

SDAR: a package for plotting and analyzing stratigraphy data in R

Stratigraphic Columns (SC) are the most useful and common ways to represent the eld descriptions (e.g., grain size, thickness of rock packages, and fossil and lithological components) of rock sequences and well logs. In these representations the width of SC vary according to the grain size (i.e., the wider the strata, the coarser the rocks (Miall 1990; Tucker 2011)), and the thickness of each layer is represented at the vertical axis of the diagram. Typically these representations are drawn 'manually' using vector graphic editors (e.g., Adobe Illustrator®, CorelDRAW®, Inskape). Nowadays there are various software which automatically plot SCs, but there are not versatile open-source tools and it is very di cult to both store and analyse stratigraphic information. This document presents Stratigraphic Data Analysis in R (SDAR), an analytical package1 designed for both plotting and facilitate the analysis of Stratigraphic Data in R (R Core Team 2014). SDAR, uses simple stratigraphic data and takes advantage of the exible plotting tools available in R to produce detailed SCs. The main bene ts of SDAR are: (i) used to generate accurate and complete SC plot including multiple features (e.g., sedimentary structures, samples, fossil content, color, structural data, contacts between beds), (ii) developed in a free software environment for statistical computing and graphics, (iii) run on a wide variety of platforms (i.e., UNIX, Windows, and MacOS), (iv) both plotting and analysing functions can be executed directly on R's command-line interface (CLI), consequently this feature enables users to integrate SDAR's functions with several others add-on packages available for R from The Comprehensive R Archive Network (CRAN).

‘Reliability Concepts in Quantile-based Analysis of Lifetime Data

Reliability analysis is a well established branch of statistics that deals with the statistical study of different aspects of lifetimes of a system of components. As we pointed out earlier that major part of the theory and applications in connection with reliability analysis were discussed based on the measures in terms of distribution function. In the beginning chapters of the thesis, we have described some attractive features of quantile functions and the relevance of its use in reliability analysis. Motivated by the works of Parzen (1979), Freimer et al. (1988) and Gilchrist (2000), who indicated the scope of quantile functions in reliability analysis and as a follow up of the systematic study in this connection by Nair and Sankaran (2009), in the present work we tried to extend their ideas to develop necessary theoretical framework for lifetime data analysis. In Chapter 1, we have given the relevance and scope of the study and a brief outline of the work we have carried out. Chapter 2 of this thesis is devoted to the presentation of various concepts and their brief reviews, which were useful for the discussions in the subsequent chapters .In the introduction of Chapter 4, we have pointed out the role of ageing concepts in reliability analysis and in identifying life distributions .In Chapter 6, we have studied the first two L-moments of residual life and their relevance in various applications of reliability analysis. We have shown that the first L-moment of residual function is equivalent to the vitality function, which have been widely discussed in the literature .In Chapter 7, we have defined percentile residual life in reversed time (RPRL) and derived its relationship with reversed hazard rate (RHR). We have discussed the characterization problem of RPRL and demonstrated with an example that the RPRL for given does not determine the distribution uniquely

Drastic variation in the surface boundary layer parameters over Cochin during the annular solar eclipse: Analysis using sonic anemometer data

Atmospheric surface boundary layer parameters vary anomalously in response to the occurrence of annular solar eclipse on 15th January 2010 over Cochin. It was the longest annular solar eclipse occurred over South India with high intensity. As it occurred during the noon hours, it is considered to be much more significant because of its effects in all the regions of atmosphere including ionosphere. Since the insolation is the main driving factor responsible for the anomalous changes occurred in the surface layer due to annular solar eclipse, occurred on 15th January 2010, that played very important role in understanding dynamics of the atmosphere during the eclipse period because of its coincidence with the noon time. The Sonic anemometer is able to give data of zonal, meridional and vertical wind as well as the air temperature at a temporal resolution of 1 s. Different surface boundary layer parameters and turbulent fluxes were computed by the application of eddy correlation technique using the high resolution station data. The surface boundary layer parameters that are computed using the sonic anemometer data during the period are momentum flux, sensible heat flux, turbulent kinetic energy, frictional velocity (u*), variance of temperature, variances of u, v and w wind. In order to compare the results, a control run has been done using the data of previous day as well as next day. It is noted that over the specified time period of annular solar eclipse, all the above stated surface boundary layer parameters vary anomalously when compared with the control run. From the observations we could note that momentum flux was 0.1 Nm 2 instead of the mean value 0.2 Nm-2 when there was eclipse. Sensible heat flux anomalously decreases to 50 Nm 2 instead of the mean value 200 Nm 2 at the time of solar eclipse. The turbulent kinetic energy decreases to 0.2 m2s 2 from the mean value 1 m2s 2. The frictional velocity value decreases to 0.05 ms 1 instead of the mean value 0.2 ms 1. The present study aimed at understanding the dynamics of surface layer in response to the annular solar eclipse over a tropical coastal station, occurred during the noon hours. Key words: annular solar eclipse, surface boundary layer, sonic anemometer

Comparing correspondence analysis and the log-ratio alternative for representing categorical data

We compare correspondance análisis to the logratio approach based on compositional data. We also compare correspondance análisis and an alternative approach using Hellinger distance, for representing categorical data in a contingency table. We propose a coefficient which globally measures the similarity between these approaches. This coefficient can be decomposed into several components, one component for each principal dimension, indicating the contribution of the dimensions to the difference between the two representations. These three methods of representation can produce quite similar results. One illustrative example is given