A study of three-dimensional curves

In certain abnormalities of spinal shape, the long axis of the spine moves out of the median sagittal plane, producing both an axial torsion and a lateral deviation out of the usual front-back plane. Clinicians need to be able to assess rapidly and accurately whether or not an individual has such an abnormality. In this paper, we examine several case studies. These are analyzed using some simple summary statistics, and the use of these methods to infer abnormality is demonstrated. Using a simple fitted model for the data, an angular estimate of abnormality is obtained. Empirical measures of abnormality based on the eigenvalues of the covariance matrix are also determined. These methods are applied to a number of individuals for whom the surface spinal profiles have been recorded and who have been assessed as normal or abnormal by clinicians.     

A wavelet approach to shape analysis for spinal curves

We present a new method to describe shape change and shape differences in curves, by constructing a deformation function in terms of a wavelet decomposition. Wavelets form an orthonormal basis which allows representations at multiple resolutions. The deformation function is estimated, in a fully Bayesian framework, using a Markov chain Monte Carlo algorithm. This Bayesian formulation incorporates prior information about the wavelets and the deformation function. The flexibility of the MCMC approach allows estimation of complex but clinically important summary statistics, such as curvature in our case, as well as estimates of deformation functions with variance estimates, and allows thorough investigation of the posterior distribution. This work is motivated by multi-disciplinary research involving a large-scale longitudinal study of idiopathic scoliosis in UK children. This paper provides novel statistical tools to study this spinal deformity, from which 5% of UK children suffer. Using the data we consider statistical inference for shape differences between normals, scoliotics and developers of scoliosis, in particular for spinal curvature, and look at longitudinal deformations to describe shape changes with time.     

Statistical techniques in stereology

Stereology typically concerns estimation of properties of a geometric structure from plane section information. This paperprovides a brief review of some statistical aspects of this rapidly developing field, with some reference to applications in the earth sciences. After an introductory discussion of the scope of stereology, section 2 briefly mentions results applicable when no assumptions can be made about the stochastic nature of the sampled matrix, statistical considerations then arising solelyfrom the ‘randomness’ of the plane section. The next two sections postulate embedded particles of specific shapes, the particular case of spheres being discussed in some detail.

References are made to results for ‘thin slices’ and other prob-ing mechanisms. Randomly located convex particles, of otherwise arbitrary shape, are discussed in section 5 and the review concludes with a specific application of stereological ideas to some data on neolithic mining.     

Smoothed functional canonical correlation analysis of humidity and temperature data

This paper focuses on smoothed functional canonical correlation analysis (SFCCA) to investigate the relationships and changes in large, seasonal and long-term data sets. The aim of this study is to introduce a guideline for SFCCA for functional data and to give some insights on the fine tuning of the methodology for long-term periodical data. The guidelines are applied on temperature and humidity data for 11 years between 2000 and 2010 and the results are interpreted. Seasonal changes or periodical shifts are visually studied by yearly comparisons. The effects of the ‘number of basis functions’ and the ‘selection of smoothing parameter’ on the general variability structure and on correlations between the curves are examined. It is concluded that the number of time points (knots), number of basis functions and the time span of evaluation (monthly, daily, etc.) should all be chosen harmoniously. It is found that changing the smoothing parameter does not have a significant effect on the structure of curves and correlations. The number of basis functions is found to be the main effector on both individual and correlation weight functions.     

Fitting Curves to Human Respiratory Data

Records of gas flow during breathing are cyclical, with the cycles varying in duration. The shape of these cycles may change with the intensity of respiratory stimulation or the development of respiratory disease, but currently research is hampered by the lack of a fully satisfactory technique for determining the shape of a typical cycle. The approach adopted here is to replace the time series by a 'phase diagram', plotting the time integral of flow against flow itself. Principal curves are then fitted. These are curves `through the middle of the data' which were introduced by Hastie and Stuetzle. The shapes of these curves are compared, either directly or after reconstructing an average cycle corresponding to each fitted curve. This has the advantage that the shape of the waveform is separated from the amplitude, and from the duration of the breath. A disadvantage is that periods of zero flow are lost, and the reconstructed average cycle may show irregularities at points near zero flow as a result. In practice, the methodology showed clear differences in shape between the protocols, gave reasonable average cycles and ordered the waveform shapes according to the hardness of breathing induced by the protocols.     

Shape change along geodesics with application to cleft lip surgery

Continuous shape change is represented as curves in the shape space. A method for checking the closeness of these curves to a geodesic is presented. Three large databases of short human motions are considered and shown to be well approximated by geodesics. The motions are thus approximated by two shapes on the geodesic and the rate of progress along the path. An analysis of facial motion data taken from a study of subjects with cleft lip or cleft palate is presented that allows the motion to be considered independently from the static shape. Inferential methods for assessing the change in motion are presented. The construction of predicted animated motions is discussed.     

Robust and scale-free effect sizes for non-Normal two-sample comparisons,with applications in e-commerce

The effect size (ES) has been mainly introduced and investigated for changes in location under an assumption of Normality for the underlying population. However, there are many circumstances where populations are non-Normal, or depend on scale and shape and not just a location parameter. Our motivating application from e-commerce requires an ES which is appropriate for long-tailed distributions. We review some common ES measures. We then introduce two novel alternative ES for two-sample comparisons, one scale-free and one on the original scale of measurement, and analyse some theoretical properties. We examine these ES for two-sample comparison studies under an assumption of Normality and investigate what happens when both location and scale parameters differ. We explore ES for phenomena for non-Normal situations, using the Weibull family for illustration. Finally, for an application, we assess differences in customer behaviour when browsing E-commerce websites.     

Testing and Valuing Dynamic Correlations for Asset Allocation

We evaluate alternative models of variances and correlations with an economic loss function. We construct portfolios to minimize predicted variance subject to a required return. It is shown that the realized volatility is smallest for the correctly specified covariance matrix for any vector of expected returns. A test of relative performance of two covariance matrices is based on work of Diebold and Mariano. The method is applied to stocks and bonds and then to highly correlated assets. On average, dynamically correct correlations are worth around 60 basis points in annualized terms, but on some days they may be worth hundreds.     

Semiparametric estimation of plane similarities: application to fast computation of aeronautic loads

In the big data era, it is often needed to resolve the problem of parsimonious data representation. In this paper, the data under study are curves and the sparse representation is based on a semiparametric model. Indeed, we propose an original registration model for noisy curves. The model is built transforming an unknown function by plane similarities. We develop a statistical method that allows to estimate the parameters characterizing the plane similarities. The properties of the statistical procedure are studied. We show the convergence and the asymptotic normality of the estimators. Numerical simulations and a real-life aeronautic example illustrate and demonstrate the strength of our methodology.     

On the measurement and analysis of asymmetry with applications to facial modelling

Summary.  Asymmetry is a feature of shape which is of particular interest in a variety of applications. With landmark data, the essential information on asymmetry is contained in the degree to which there is a mismatch between a landmark configuration and its relabelled and matched reflection. This idea is explored in the context of a study of facial shape in infants, where particular interest lies in identifying changes over time and in assessing residual deformity in children who have had corrective surgery for a cleft lip or cleft lip and palate. Interest lies not in whether the mean shape is asymmetric but in comparing the degrees of asymmetry in different populations. A decomposition of the asymmetry score into components that are attributable to particular features of the face is proposed. A further decomposition allows different sources of asymmetry due to position, orientation or intrinsic asymmetry to be identified for each feature. The methods are also extended to data representing anatomical curves across the face.     

Functional principal component analysis of spatially correlated data

This paper focuses on the analysis of spatially correlated functional data. We propose a parametric model for spatial correlation and the between-curve correlation is modeled by correlating functional principal component scores of the functional data. Additionally, in the sparse observation framework, we propose a novel approach of spatial principal analysis by conditional expectation to explicitly estimate spatial correlations and reconstruct individual curves. Assuming spatial stationarity, empirical spatial correlations are calculated as the ratio of eigenvalues of the smoothed covariance surface Cov\((X_i(s),X_i(t))\) and cross-covariance surface Cov\((X_i(s), X_j(t))\) at locations indexed by i and j. Then a anisotropy Matérn spatial correlation model is fitted to empirical correlations. Finally, principal component scores are estimated to reconstruct the sparsely observed curves. This framework can naturally accommodate arbitrary covariance structures, but there is an enormous reduction in computation if one can assume the separability of temporal and spatial components. We demonstrate the consistency of our estimates and propose hypothesis tests to examine the separability as well as the isotropy effect of spatial correlation. Using simulation studies, we show that these methods have some clear advantages over existing methods of curve reconstruction and estimation of model parameters.     

Bayesian analysis of human movement curves

Summary.  We consider the Bayesian analysis of human movement data, where the subjects perform various reaching tasks. A set of markers is placed on each subject and a system of cameras records the three-dimensional Cartesian co-ordinates of the markers during the reaching movement. It is of interest to describe the mean and variability of the curves that are traced by the markers during one reaching movement, and to identify any differences due to covariates. We propose a methodology based on a hierarchical Bayesian model for the curves. An important part of the method is to obtain identifiable features of the movement so that different curves can be compared after temporal warping. We consider four landmarks and a set of equally spaced pseudolandmarks are located in between. We demonstrate that the algorithm works well in locating the landmarks, and shape analysis techniques are used to describe the posterior distribution of the mean curve. A feature of this type of data is that some parts of the movement data may be missing—the Bayesian methodology is easily adapted to cope with this situation.     

A concentration approach to sensitivity studies in statistical estimation problems

It is shown that the concept of concentration is of potential interest in the sensitivity study of some parameters and related estimators. Basic ideas are introduced for a real parameter θ>0 together with graphical representations using Lorenz curves of concentration. Examples based on the mean, standard deviation and variance are provided for some classical distributions. This concentration approach is also discussed in relation with influence functions. Special emphasis is given to the average concentration of an estimator which provides a sensitivity measure allowing one to compare several estimators of the same parameter. Properties of this measure are investigated through simulation studies and its practical interest is illustrated by examples based on the trimmed mean and the Winsorized variance.     

Fitting Roc Curves Using Non-linear Binomial Regression

The performance of a diagnostic test is summarized by its receiver operating characteristic (ROC) curve. Empirical data on a test's performance often come in the form of observed true positive and false positive relative frequencies, under varying conditions. This paper describes a family of models for analysing such data. The underlying ROC curves are specified by a shift parameter, a shape parameter and a link function. Both the position along the ROC curve and the shift parameter are modelled linearly. The shape parameter enters the model non-linearly but in a very simple manner. One simple application is to the meta-analysis of independent studies of the same diagnostic test, illustrated on some data of Moses, Shapiro & Littenberg (1993). A second application to so-called vigilance data is given, where ROC curves differ across subjects, and modelling of the position along the ROC curve is of primary interest.     

Longitudinal shape analysis by using the spherical coordinates

One of the important topics in morphometry that received high attention recently is the longitudinal analysis of shape variation. According to Kendall's definition of shape, the shape of object appertains on non-Euclidean space, making the longitudinal study of configuration somehow difficult. However, to simplify this task, triangulation of the objects and then constructing a non-parametric regression-type model on the unit sphere is pursued in this paper. The prediction of the configurations in some time instances is done using both properties of triangulation and the size of great baselines. Moreover, minimizing a Euclidean risk function is proposed to select feasible weights in constructing smoother functions in a non-parametric smoothing manner. These will provide some proper shape growth models to analysis objects varying in time. The proposed models are applied to analysis of two real-life data sets.     

Ranges of Measures of Association for Familial Binary Variables

Familial binary data occur in a wide range of scientific investigations. Numerous measures of association have been proposed in the literature for the study of intra-family dependence of the binary variables. These measures include correlations, odd ratios, kappa statistics, and relative risks. We study the permissible ranges of these measures of association such that a joint distribution exists for the familial binary variables. Our results are useful for developing efficient estimation methods for the measures of association.     

A simple approach for generating correlated binary variates ∗

Correlated binary data arise frequently in medical as well as other scientific disciplines; and statistical methods, such as generalized estimating equation (GEE), have been widely used for their analysis. The need for simulating correlated binary variates arises for evaluating small sample properties of the GEE estimators when modeling such data. Also, one might generate such data to simulate and study biological phenomena such as tooth decay or periodontal disease. This article introduces a simple method for generating pairs of correlated binary data. A simple algorithm is also provided for generating an arbitrary dimensional random vector of non-negatively correlated binary variates. The method relies on the idea that correlations among the random variables arise as a result of their sharing some common components that induce such correlations. It then uses some properties of the binary variates to represent each variate in terms of these common components in addition to its own elements. Unlike most previous approaches that require solving nonlinear equations or use some distributional properties of other random variables, this method uses only some properties of the binary variate. As no intermediate random variables are required for generating the binary variates, the proposed method is shown to be faster than the other methods. To verify this claim, we compare the computational efficiency of the proposed method with those of other procedures.     

On statistical problems with face identification from photographs

SUMMARY Automatic identification of faces from a database given a digital view is becoming increasingly important. The question arises whether or not there can be a face identification system similar to the fingerprinting system, where a certain number of matches are regarded as sufficient to identify the person in the database. We first give a very general review of the topic of facial measurements and indicate some deep statistical problems. We then analyze a database of photographs. Certain characteristics of the population are provided, such as the modes of variation and correlation structures using shape analysis. The data involve angles as well as distances. The principal component analysis for angular data is discussed, its conversion into landmark data is established and the two approaches are compared. A new approach of anchor shape analysis for specialized distances is discussed.     

On ε-contaminated priors with quantile and piece-wise unimodality constraints

In this work we examine the e-contamination model of prior densities γ={π:π=(1-ε)π₀(θ)+εq: qεG}, where π₀(θ) is the base elicited prior, q is a contamination belonging to some suitable class G and ε reflects the amount of error in π₀(θ). Various classes with shape and/or quantile constraints are analysed, and a posterior robust analysis is carried out. It turns out that quantile restrictions alone do not produce asymptotical rational behaviour, so it is unavoidable to introduce shape constraints as well. The conclusions are in line with those of O'Hagan and Berger (1988). Illustrations related to testing hypothesis and likelihood sets are given.     

Analysis of longitudinal data using a finite mixture model

This paper considers a finite mixture model for longitudinal data, which can be used to study the dependency of the shape of the respective follow-up curves on treatments or other influential factors and to classify these curves. An EM-algorithm to achieve the ml-estimate of the model is given. The potencies of the model are demonstrated using data of a clinical trial.