Regression for non-Euclidean data using distance matrices

Regression methods for common data types such as measured, count and categorical variables are well understood but increasingly statisticians need ways to model relationships between variable types such as shapes, curves, trees, correlation matrices and images that do not fit into the standard framework. Data types that lie in metric spaces but not in vector spaces are difficult to use within the usual regression setting, either as the response and/or a predictor. We represent the information in these variables using distance matrices which requires only the specification of a distance function. A low-dimensional representation of such distance matrices can be obtained using methods such as multidimensional scaling. Once these variables have been represented as scores, an internal model linking the predictors and the responses can be developed using standard methods. We call scoring as the transformation from a new observation to a score, whereas backscoring is a method to represent a score as an observation in the data space. Both methods are essential for prediction and explanation. We illustrate the methodology for shape data, unregistered curve data and correlation matrices using motion capture data from an experiment to study the motion of children with cleft lip.     

Performance models for unreliable flexible manufacturing systems

This paper deals with the analysis and application of queueing models for single and multi stage flexible manufacturing systems that are subject to resource failure. Exact results are derived for the single stage queueing system. Two approximations are presneted for the multi stage queueing system as a closed network. We validate the approximations by comparing their performance estimates against the exact global balance solution.     

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.     

Working Together to Improve Accessibility: Consortial E-Resource Accessibility and Advocacy

Abstract

In 2015, the Big Ten Academic Alliance (BTAA) Libraries formed the BTAA Library E-Resource Accessibility Group. This group, which consists of members from each Big Ten library and the BTAA Library Initiatives staff, was charged with creating boilerplate accessibility license language for vendor contracts, as well as collectively funding high level accessibility testing of e-resources and posting these publicly for all libraries and users. Since the creation of the group, about 50 databases have been tested and accessibility license language has been created that can be used and adapted by anyone. In 2019, the group welcomed another consortia, the Association of Southeastern Research Libraries (ASERL), to be partners in this work, renaming themselves the Library Accessibility Alliance. This paper examines the mission, accomplishments, and new partnership in more depth while also discussing the need for focused efforts to help ensure accessible e-resources.     

Time series forecasting with neural networks: a comparative study using the air line data

This case-study fits a variety of neural network (NN) models to the well-known air line data and compares the resulting forecasts with those obtained from the Box–Jenkins and Holt–Winters methods. Many potential problems in fitting NN models were revealed such as the possibility that the fitting routine may not converge or may converge to a local minimum. Moreover it was found that an NN model which fits well may give poor out-of-sample forecasts. Thus we think it is unwise to apply NN models blindly in 'black box' mode as has sometimes been suggested. Rather, the wise analyst needs to use traditional modelling skills to select a good NN model, e.g. to select appropriate lagged variables as the 'inputs'. The Bayesian information criterion is preferred to Akaike's information criterion for comparing different models. Methods of examining the response surface implied by an NN model are examined and compared with the results of alternative nonparametric procedures using generalized additive models and projection pursuit regression. The latter imposes less structure on the model and is arguably easier to understand.     

我为电影狂

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Straightforward intermediate rank tensor product smoothing in mixed models

Tensor product smooths provide the natural way of representing smooth interaction terms in regression models because they are invariant to the units in which the covariates are measured, hence avoiding the need for arbitrary decisions about relative scaling of variables. They would also be the natural way to represent smooth interactions in mixed regression models, but for the fact that the tensor product constructions proposed to date are difficult or impossible to estimate using most standard mixed modelling software. This paper proposes a new approach to the construction of tensor product smooths, which allows the smooth to be written as the sum of some fixed effects and some sets of i.i.d. Gaussian random effects: no previously published construction achieves this. Because of the simplicity of this random effects structure, our construction is useable with almost any flexible mixed modelling software, allowing smooth interaction terms to be readily incorporated into any Generalized Linear Mixed Model. To achieve the computationally convenient separation of smoothing penalties, the construction differs from previous tensor product approaches in the penalties used to control smoothness, but the penalties have the advantage over several alternative approaches of being explicitly interpretable in terms of function shape. Like all tensor product smoothing methods, our approach builds up smooth functions of several variables from marginal smooths of lower dimension, but unlike much of the previous literature we treat the general case in which the marginal smooths can be any quadratically penalized basis expansion, and there can be any number of them. We also point out that the imposition of identifiability constraints on smoothers requires more care in the mixed model setting than it would in a simple additive model setting, and show how to deal with the issue. An interesting side effect of our construction is that an ANOVA-decomposition of the smooth can be read off from the estimates, although this is not our primary focus. We were motivated to undertake this work by applied problems in the analysis of abundance survey data, and two examples of this are presented.     

Benedict Cumberbatch:天才最性感

泰戈尔在某部小说中说,女性手中的花环,要么挂在禽兽的恶汉脖子上,要么献给强同虚构人手般空想。因为女性在心底深处,渴望被肉欲的车轮碾碎,又或者在思想的烈火中融化--这种言辞似乎过于极端,好像女人都像受虐狂--不过,女人确实热爱幻想田人的强大,或肉体,或精神.