Statistical Analysis Framework Research Articles

LEAKAGE POWER REDUCTION THROUGH DUAL Vth ASSIGNMENT CONSIDERING THRESHOLD VOLTAGE VARIATION

With technology scaling, leakage power has become an important part of the total power consumption, affecting both yields and lifetime of digital circuits. Dual V th assignment, which was an effective method to reduce leakage power in the past, is also effective in today's technologies with certain modifications. In this paper, based on our statistical timing analysis (SSTA) framework, we presented a dual V th assignment method which can effectively reduce the leakage power even in the presence of large V th variation. Besides, taking the correlation between gates into account, we propose a novel statistical DAG pruning method to speed up the dual V th assignment algorithm. Experimental results show that statistical dual V th assignment can reduce on average 95% and 40% more leakage current compared with the conventional static method, without affecting the performance constraints under 180 nm and 90 nm technology nodes. Also our statistical DAG pruning method can reduce 30% gates in the circuit on average and save up to 50% of the total runtime in the 90 nm technology node.

ROOT — A C++ framework for petabyte data storage, statistical analysis and visualization

ROOT is an object-oriented C++ framework conceived in the high-energy physics (HEP) community, designed for storing and analyzing petabytes of data in an efficient way. Any instance of a C++ class can be stored into a ROOT file in a machine-independent compressed binary format. In ROOT the TTree object container is optimized for statistical data analysis over very large data sets by using vertical data storage techniques. These containers can span a large number of files on local disks, the web, or a number of different shared file systems. In order to analyze this data, the user can chose out of a wide set of mathematical and statistical functions, including linear algebra classes, numerical algorithms such as integration and minimization, and various methods for performing regression analysis (fitting). In particular, the RooFit package allows the user to perform complex data modeling and fitting while the RooStats library provides abstractions and implementations for advanced statistical tools. Multivariate classification methods based on machine learning techniques are available via the TMVA package. A central piece in these analysis tools are the histogram classes which provide binning of one- and multi-dimensional data. Results can be saved in high-quality graphical formats like Postscript and PDF or in bitmap formats like JPG or GIF. The result can also be stored into ROOT macros that allow a full recreation and rework of the graphics. Users typically create their analysis macros step by step, making use of the interactive C++ interpreter CINT, while running over small data samples. Once the development is finished, they can run these macros at full compiled speed over large data sets, using on-the-fly compilation, or by creating a stand-alone batch program. Finally, if processing farms are available, the user can reduce the execution time of intrinsically parallel tasks — e.g. data mining in HEP — by using PROOF, which will take care of optimally distributing the work over the available resources in a transparent way. Program summary Program title: ROOT Catalogue identifier: AEFA_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEFA_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: LGPL No. of lines in distributed program, including test data, etc.: 3 044 581 No. of bytes in distributed program, including test data, etc.: 36 325 133 Distribution format: tar.gz Programming language: C++ Computer: Intel i386, Intel x86-64, Motorola PPC, Sun Sparc, HP PA-RISC Operating system: GNU/Linux, Windows XP/Vista, Mac OS X, FreeBSD, OpenBSD, Solaris, HP-UX, AIX Has the code been vectorized or parallelized?: Yes RAM: > 55 Mbytes Classification: 4, 9, 11.9, 14 Nature of problem: Storage, analysis and visualization of scientific data Solution method: Object store, wide range of analysis algorithms and visualization methods Additional comments: For an up-to-date author list see: http://root.cern.ch/drupal/content/root-development-team and http://root.cern.ch/drupal/content/former-root-developers Running time: Depending on the data size and complexity of analysis algorithms

A Riemannian Framework for Statistical Shape Analysis of the Corpus Callosum.

A Riemannian Framework for Statistical Shape Analysis of the Corpus Callosum.

Sensitivity analysis of vibro-acoustic systems in statistical energy analysis framework

The main objective of this paper is to present first and second-order sensitivity analysis of vibro-acoustic systems in the statistical energy analysis (SEA) frame work. Equations for computing these sensitivities for a general SEA model are obtained from two different approaches: (1) direct method and (2) adjoint method. The above equations are applied to a simple model of three plates, joined in the form of a ‘Z’, to minimize the total energy of one of the plates. It has been verified that these approaches lead to the same results and the difference between them is only with respect to the computational efficiency. The design sensitivity results calculated from the proposed analytical methods are compared with those obtained from the finite difference method, which show good agreement. The results of this paper can be useful to optimization of vibro-acoustic systems at the drawing board stage in the SEA framework.

Characterizing the Distribution of Safety Occurrences in Aviation

The demand for air travel continues to grow rapidly, and by 2010, air traffic in Europe is expected to be twice the 1990 level. This increase in air traffic may adversely affect safety if appropriate measures are not taken. Regulators and Air Navigation Service Providers (ANSPs) analyze incidents to identify ways to prevent them from happening again. Incidents are rare events in airspace, and this poses problems with robust statistical testing and trend analysis. Aviation safety analysis uses the Poisson distribution where possible, but doubts remain as to its appropriateness. Incident data from ANSP were analyzed over 17 years in a logical quantitative manner using extreme value theory (EVT), a method that uses the limited amount of information available on incidents and defines a distribution that can be used to make statistical inferences. EVT has had considerable use in other fields but little in aviation. A statistical analysis and validation framework is outlined and used to test the data using Poisson, negative binomial, and EVT. The goodness-of-fit tests, as well as other statistical tests, indicate that by far the best fit of the data is achieved using EVT. Further analysis using EVT shows its efficacy as a tool for monitoring and predicting incidents, based on statistical hypothesis and the use of quantile information.

Room requirements for laboratory measurement of transmission loss and impact sound insulation.

Laboratory measurement of transmission loss (ASTM E90) and impact sound insulation (ASTM E492) have one thing in common: measurement of the sound power radiated into the receive room by the specimen under test. In E90 the reported quantity, transmission loss (TL), is the difference between the incident and transmitted sound power, whereas for E492 the reported quantity, normalized impact sound pressure level (NISPL), is a direct measure of the radiated sound power of the floor. A derivation of the TL and NISPL equations using a statistical energy analysis (SEA) framework shows that the ASTM equations are based on the assumption of power balance and statistical sampling of the sound field. This SEA framework is used to define the acoustical conditions of the receive room (number of modes in each frequency band, the average loss factor in each band, and hence the modal overlap factor), which are then related to the physical properties such as room shape, volume, absorption, etc. The conditions necessary for...

A Statistical Framework for the Sensitivity Analysis of Radiative Transfer Models

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Process models are widely used tools, both for studying fundamental processes themselves and as elements of larger system studies. A radiative transfer model (RTM) simulates the interaction of light with a medium. We are interested in RTMs that model light reflected from a vegetated region. Such an RTM takes as input various biospheric and illumination parameters and computes the upwelling radiation at the top of the canopy. The question we address is as follows: Which of the inputs to the RTM has the greatest impact on the computed observation? We study the leaf canopy model (LCM) RTM, which was designed to study the feasibility of observing leaf chemistry remotely. Its inputs are leaf chemistry variables (chlorophyll, water, lignin, and cellulose) and canopy structural parameters (leaf area index, leaf angle distribution, soil reflectance, and sun angle). We present a statistical approach to the sensitivity analysis of RTMs to answer the question previously posed. The focus is on global sensitivity analysis, studying how the RTM output changes as the inputs vary continuously according to a probability distribution over the input space. The influence of each input variable is captured through the “main effects” and “sensitivity indices.” Direct computation requires extensive computationally expensive runs of the RTM. We develop a Gaussian process approximation to the RTM output to enable efficient computation. We illustrate how the approach can effectively determine the inputs that are vital for accurate prediction. The methods are applied to the LCM with seven inputs and output obtained at eight wavelengths associated with Moderate-resolution Imaging Spectroradiometer bands that are sensitive to vegetation. </para>

Significance Analysis of Spectral Count Data in Label-free Shotgun Proteomics

Spectral counting has become a commonly used approach for measuring protein abundance in label-free shotgun proteomics. At the same time, the development of data analysis methods has lagged behind. Currently most studies utilizing spectral counts rely on simple data transforms and posthoc corrections of conventional signal-to-noise ratio statistics. However, these adjustments can neither handle the bias toward high abundance proteins nor deal with the drawbacks due to the limited number of replicates. We present a novel statistical framework (QSpec) for the significance analysis of differential expression with extensions to a variety of experimental design factors and adjustments for protein properties. Using synthetic and real experimental data sets, we show that the proposed method outperforms conventional statistical methods that search for differential expression for individual proteins. We illustrate the flexibility of the model by analyzing a data set with a complicated experimental design involving cellular localization and time course.

Toward a Common Framework for Statistical Analysis and Development

We develop a general ontology of statistical methods and use it to propose a common framework for statistical analysis and software development built on and within the R language, including R's numerous existing packages. This framework offers a simple unified structure and syntax that can encompass a large fraction of existing statistical procedures. We conjecture that it can be used to encompass and present simply a vast majority of existing statistical methods, without requiring changes in existing approaches, and regardless of the theory of inference on which they are based, notation with which they were developed, and programming syntax with which they have been implemented. This development enabled us, and should enable others, to design statistical software with a single, simple, and unified user interface that helps overcome the conflicting notation, syntax, jargon, and statistical methods existing across the methods subfields of numerous academic disciplines. The approach also enables one to build a graphical user interface that automatically includes any method encompassed within the framework. We hope that the result of this line of research will greatly reduce the time from the creation of a new statistical innovation to its widespread use by applied researchers whether or not they use or program in R.

Epidemiological inference for partially observed epidemics: The example of the 2001 foot and mouth epidemic in Great Britain

This paper develops a statistical framework for a retrospective analysis for well-observed livestock epidemics during which intervention policies may conceal cases, thus potentially biasing naively derived parameter and final size estimates. We apply the methods to the 2001 foot and mouth epidemic (FMD) in Great Britain, during which a large number of farms (about 7500) were pre-emptively culled as part of the control effort without ever being diagnosed as being infected. We infer farm-level infectivity and susceptibility parameters, a distribution for the delay from infection to report, together with a time varying farm infectivity profile for farms. Hidden infections among proactively culled farms were accounted for using a data augmentation approach utilising reversible jump MCMC methods. Simulated epidemics derived using the parameter estimates obtained reproduced the 2001 epidemic well. Our analysis demonstrates that time-varying infectivity profiles fit the 2001 data better than naive assumptions of constant infectiousness. We estimate that around 210 (or 2.8%) of the farms proactively culled in the 2001 epidemic were infected. However, for the parameter estimated obtained, preliminary simulation results indicate that had contiguous culling not been applied in 2001, the epidemic might have been substantially larger.

Decomposing health: tolerance and resistance to parasites in animals

Plant biologists have long recognized that host defence against parasites and pathogens can be divided into two conceptually different components: the ability to limit parasite burden (resistance) and the ability to limit the harm caused by a given burden (tolerance). Together these two components determine how well a host is protected against the effects of parasitism. This distinction is useful because it recognizes that hosts that are best at controlling parasite burdens are not necessarily the healthiest. Moreover, resistance and tolerance can be expected to have different effects on the epidemiology of infectious diseases and host-parasite coevolution. However, studies of defence in animals have to date focused on resistance, whereas the possibility of tolerance and its implications have been largely overlooked. The aim of our review is to (i) describe the statistical framework for analysis of tolerance developed in plant science and how this can be applied to animals, (ii) review evidence of genetic and environmental variation for tolerance in animals, and studies indicating which mechanisms could contribute to this variation, and (iii) outline avenues for future research on this topic.

Assessment of Peri-Articular Implant Fitting Based on Statistical Finite Element Modeling

We present a framework for statistical finite element analysis allowing performing statistical statements of biomechanical performance of peri-articular implants across a given population. In this paper, we focus on the design of orthopaedic implants that fit a maximum percentage of the target population, both in terms of geometry and biomechanical stability. CT scans of the bone under consideration are registered non-rigidly to obtain correspondences in position between them. A statistical model of shape is computed by means of principal component analysis. A method to automatically propagate standardize fractures on the statistically-based bone population has been developed as well as tools to optimize implant position to best-fit the bone surface. Afterwards, finite element analysis is performed to analyse the biomechanical performance of the bone/implant construct. The mechanical behaviour of different PCA bone instances is compared for tibia representing the Asian and Caucasian populations.

A general framework for the statistical exploration of the ecological niche

We propose a new statistical framework for the exploratory analysis of the ecological niche, the “General niche-environment system factor analysis” (GNESFA). The data required for this analysis are (i) a table giving the values of the environmental variables in each environment unit (EU, e.g., the patches of habitat on a vector map), (ii) a set of weights measuring the availability of the EUs to the species (e.g., the proportion of the study area covered by a given patch), and (iii) a set of utilization weights describing the use of the EUs by the focal species (e.g., the proportion of detections of the species in each patch). Each row of the table corresponds to a point in the multidimensional space defined by the environmental variables, and each point is associated with two weights. The GNESFA searches the directions in this space where the two weight distributions differ the most, choosing one distribution as the reference, and the other one as the focus. The choice of the utilization as the reference corresponds to the MADIFA (Mahalanobis distances factor analysis), which identifies the directions on which the available EUs are in average the furthest from the optimum of the niche, allowing habitat suitability modelling. The choice of the availability as the reference corresponds to the FANTER (Factor analysis of the niche, taking the environment as the reference), which identifies the directions on which the niche is the furthest from the average environment (marginality) and those on which the niche is the narrowest compared with the environment (specialization). The commonly used ENFA (Ecological niche factor analysis) is at the middle point between the MADIFA and the FANTER, considering both distributions as the reference and the focus simultaneously. When used concurrently, these three analyses allow an extensive exploration of the system.

Identification of cancer genes using a statistical framework for multi-experiment analysis of non-discretized array CGH data

Identification of cancer genes using a statistical framework for multi-experiment analysis of non-discretized array CGH data

Object oriented data analysis: Sets of trees

Object oriented data analysis is the statistical analysis of populations of complex objects. In the special case of functional data analysis, these data objects are curves, where standard Euclidean approaches, such as principal component analysis, have been very successful. Recent developments in medical image analysis motivate the statistical analysis of populations of more complex data objects which are elements of mildly non-Euclidean spaces, such as Lie groups and symmetric spaces, or of strongly non-Euclidean spaces, such as spaces of tree-structured data objects. These new contexts for object oriented data analysis create several potentially large new interfaces between mathematics and statistics. This point is illustrated through the careful development of a novel mathematical framework for statistical analysis of populations of tree-structured objects.

Neighborhoods, Ethnicity and School Choice: Developing a Statistical Framework for Geodemographic Analysis

Geodemographics as the “analysis of people by where they live” has origins in urban sociology and social mapping, and is experiencing a renaissance in applied spatial demography. However, some commentators have expressed reservations about the statistical limitations of common geodemographic practices, especially focusing on the potential internal heterogeneity of the geodemographic groupings, as well as the problem of clearly identifying predictor variables that might account for or explain the socioeconomic patterns revealed by geodemographic analyses. In this paper we argue that geodemographic typologies are structured methods for making sense of the spatial and socioeconomic patterns encoded within complex datasets such as national census data. By treating geodemographics as more a framework than a tool for analysis in its own right we are able to integrate it with the flexibility and statistical conventions offered by multilevel modeling. We demonstrate this with a case study of whether pupils from different types of neighborhood in Birmingham, England are more or less likely to attend their nearest state-funded secondary school and how that likelihood varies with the ethnic composition of the neighborhood. In so doing we build on previous research suggesting that ethnic segregation between schools is at least equal to that between neighborhoods in England and speculate in this regard on the consequences of current government plans to extend choice to parents within a schools market.

ECSI – Customer Satisfaction Modelling and Analysis: A Case Study

In this paper we analyse the European Customer Satisfaction Index (ECSI) model measured on five different customer groups. In the analysis we use Structural Equation Modelling, which is a general and convenient framework for statistical analysis, including traditional multivariate procedures such as factor analysis or multiple regression modelling. To estimate the structural coefficients, a Multilevel Regression Model is used, which facilitates the analysis of hierarchical data, where observations may be nested within higher levels of classification. In this analysis the hierarchical structure is represented by the customers nested in the companies, so we have two levels of analysis, a micro level nested in a macro level.

Quantitative magnetic resonance image analysis via the EM algorithm with stochastic variation

Quantitative Magnetic Resonance Imaging (qMRI) provides researchers insight into pathological and physiological alterations of living tissue, with the help of which, researchers hope to predict (local) therapeutic efficacy early and determine optimal treatment schedule. However, the analysis of qMRI has been limited to ad-hoc heuristic methods. Our research provides a powerful statistical framework for image analysis and sheds light on future localized adaptive treatment regimes tailored to the individual's response. We assume in an imperfect world we only observe a blurred and noisy version of the underlying pathological/physiological changes via qMRI, due to measurement errors or unpredictable influences. We use a hidden Markov Random Field to model the spatial dependence in the data and develop a maximum likelihood approach via the Expectation-Maximization algorithm with stochastic variation. An important improvement over previous work is the assessment of variability in parameter estimation, which is the valid basis for statistical inference. More importantly, we focus on the expected changes rather than image segmentation. Our research has shown that the approach is powerful in both simulation studies and on a real dataset, while quite robust in the presence of some model assumption violations.

A Quadratic Modeling-Based Framework for Accurate Statistical Timing Analysis Considering Correlations

The impact of parameter variations on timing due to process variations has become significant in recent years. In this paper, we present a statistical timing analysis (STA) framework with quadratic gate delay models that also captures spatial correlations. Our technique does not make any assumption about the distribution of the parameter variations, gate delays, and arrival times. We propose a Taylor-series expansion-based quadratic representation of gate delays and arrival times which are able to effectively capture the nonlinear dependencies that arise due to increasing parameter variations. In order to reduce the computational complexity introduced due to quadratic modeling during STA, we also propose an efficient linear modeling driven quadratic STA scheme. We ran two sets of experiments assuming the global parameters to have uniform and Gaussian distributions, respectively. On an average, the quadratic STA scheme had 20.5times speedup in runtime as compared to Monte Carlo simulations with an rms error of 0.00135 units between the two timing cummulative density functions (CDFs). The linear modeling driven quadratic STA scheme had 51.5times speedup in runtime as compared to Monte Carlo simulations with an rms error of 0.0015 units between the two CDFs. Our proposed technique is generic and can be applied to arbitrary variations in the underlying parameters under any spatial correlation model

Integrated Traffic Simulation–Statistical Analysis Framework for Online Prediction of Freeway Travel Time

This paper introduces a novel approach to the online short-term prediction of point-to-point freeway travel time, combining statistical forecasting techniques with traffic simulation. At every freeway entrance point, a time series analysis model based on traffic detector counts is used to predict traffic demands, whose flow through the freeway segment is simulated by a cell transmission model. This procedure, applied within a rolling-horizon framework, generates online travel time predictions consistent with traffic flow theory. Experimental results obtained from synthetic data strongly suggest that the estimates obtained with this methodology are robust and accurate. For a wide range of congestion conditions and freeway segment lengths, more than half of the predictions errors were found to be smaller than 15%. Moreover, 80% of these errors fell below 40 s when the actual travel times ranged between 3 and 10 min. Further analyses of the model sensitivity to traffic detector coverage revealed that detector separations of approximately 1 mi resulted in the most precise travel time estimates. In addition to its satisfactory performance, the proposed framework is flexible, and it can make use of additional online data and easily incorporate different forecasting and simulation techniques. Therefore, this work provides a powerful tool for online travel time prediction, suitable for a variety of practical implementation conditions and requirements.