Binary Models for Marginal Independence

Log-linear models are a classical tool for the analysis of contingency tables. In particular, the subclass of graphical log-linear models provides a general framework for modelling conditional independences. However, with the exception of special structures, marginal independence hypotheses cannot be accommodated by these traditional models. Focusing on binary variables, we present a model class that provides a framework for modelling marginal independences in contingency tables. The approach taken is graphical and draws on analogies to multivariate Gaussian models for marginal independence. For the graphical model representation we use bi-directed graphs, which are in the tradition of path diagrams. We show how the models can be parameterized in a simple fashion, and how maximum likelihood estimation can be performed using a version of the Iterated Conditional Fitting algorithm. Finally we consider combining these models with symmetry restrictions

Tensor Regression with Applications in Neuroimaging Data Analysis

Classical regression methods treat covariates as a vector and estimate a corresponding vector of regression coefficients. Modern applications in medical imaging generate covariates of more complex form such as multidimensional arrays (tensors). Traditional statistical and computational methods are proving insufficient for analysis of these high-throughput data due to their ultrahigh dimensionality as well as complex structure. In this article, we propose a new family of tensor regression models that efficiently exploit the special structure of tensor covariates. Under this framework, ultrahigh dimensionality is reduced to a manageable level, resulting in efficient estimation and prediction. A fast and highly scalable estimation algorithm is proposed for maximum likelihood estimation and its associated asymptotic properties are studied. Effectiveness of the new methods is demonstrated on both synthetic and real MRI imaging data.Comment: 27 pages, 4 figure

Autobiography as unconventional history: Constructing the author

The experience of historians as autobiographers has led them to reconsider the nature of historical knowledge and the function of the historian as an intermediary between the past and present. In the new theoretical context of the social sciences and historiography, we can take this proposal further and consider autobiography as a valid form of history—or, at least, as ‘unconventional history’, understood as negotiations with history that transcend or subvert traditional chronological monographs, posit the ‘subjective’ as a useful form of knowledge, and engage the constructed nature of the text. Taking this hypothesis as a starting point, this article reads historians' autobiographical texts to explore if we can/should continue to defend the classic distinction between subject and object, historian scientist and historian author. In this article I compare the work of several historian autobiographers that permit us to identify different methodologies in approaching the story of the self that also reflects different theoretical conceptions of history. I argue that historians that may be considered ‘constructionist’, such as Fernand Braudel, Annie Kriegel, George Duby, and Eric Hobsbawm, design their autobiographies in the same way they articulate their historical texts: by foregrounding objectivity and establishing critical distance between the subject—the historian who narrates the story—and the object—one's own life. Unconventional or experimental approaches, such as those espoused by Robert Rosenstone, Dominick LaCapra, or Clifford Geertz, result in more self-conscious autobiographies, which are, paradoxically, often more realistic and more revealing of the epistemological nature of life writing. ----------------- La experiencia de los historiadores como autobiógrafos les ha llevado a reconsiderar la naturaleza del conocimiento histórico y la función del historiador como un intermediario entre el pasado y el presente. En el nuevo contexto teórico de las ciencias sociales y la historiografía podemos tomar esta propuesta más allá y considerar la autobiografía como una forma válida de historia-o, al menos, de historia ‘poco convencional’-, entendida como negociaciones con la historia que trascienden o subvierten las tradicionales monografías cronológicas, plantean lo "subjetivo" como una forma útil de conocimiento y participan de la naturaleza construida del texto. Tomando esta hipótesis como punto de partida, este artículo lee los textos autobiográficos de los historiadores para explorar si se puede / debe seguir defendiendo la clásica distinción entre sujeto y objeto, historiador científico e historiador escritor. En este artículo comparo el trabajo de varios historiadores autobiógrafos que nos permiten identificar las diferentes metodologías para acercarse a la historia del yo y que también reflejan las diferentes concepciones teóricas de la historia. Sostengo que los historiadores que pueden considerarse "constructivistas", como Fernand Braudel, Annie Kriegel, George Duby y Eric Hobsbawm, diseñan sus autobiografías de la misma forma que articulan sus textos históricos: poniendo en primer plano la objetividad y estableciendo una distancia crítica entre el sujeto -el historiador que narra la historia-y el objeto- la vida de cada uno. Enfoques no convencionales o experimentales, como los expuestos por Robert Rosenstone, Dominick LaCapra, o Clifford Geertz, resultan autobiografías más autoconscientes, que son, paradójicamente, a menudo más realistas y más reveladoras de la naturaleza epistemológica de la escritura de la vida

Interventions to Promote Cancer Awareness and Early Presentation: Systematic Review

Low cancer awareness contributes to delay in presentation for cancer symptoms and may lead to delay in cancer diagnosis. The aim of this study was to review the evidence for the effectiveness of interventions to raise cancer awareness and promote early presentation in cancer to inform policy and future research. We searched bibliographic databases and reference lists for randomised controlled trials of interventions delivered to individuals, and controlled or uncontrolled studies of interventions delivered to communities. We found some evidence that interventions delivered to individuals modestly increase cancer awareness in the short term and insufficient evidence that they promote early presentation. We found limited evidence that public education campaigns reduce stage at presentation of breast cancer, malignant melanoma and retinoblastoma

Computational Model for Urban Growth Using Socioeconomic Latent Parameters

Land use land cover changes (LULCC) are generally modeled using multi-scale spatio-temporal variables. Recently, Markov Chain (MC) has been used to model LULCC. However, the model is derived from the proportion of LULCC observed over a given period and it does not account for temporal factors such as macro-economic, socio-economic, etc. In this paper, we present a richer model based on Hidden Markov Model (HMM), grounded in the common knowledge that economic, social and LULCC processes are tightly coupled. We propose a HMM where LULCC classes represent hidden states and temporal fac-tors represent emissions that are conditioned on the hidden states. To our knowledge, HMM has not been used in LULCC models in the past. We further demonstrate its integration with other spatio-temporal models such as Logistic Regression. The integrated model is applied on the LULCC data of Pune district in the state of Maharashtra (India) to predict and visualize urban LULCC over the past 14 years. We observe that the HMM integrated model has improved prediction accuracy as compared to the corresponding MC integrated modelComment: 12 page

Combining frequency and time domain approaches to systems with multiple spike train input and output

A frequency domain approach and a time domain approach have been combined in an investigation of the behaviour of the primary and secondary endings of an isolated muscle spindle in response to the activity of two static fusimotor axons when the parent muscle is held at a fixed length and when it is subjected to random length changes. The frequency domain analysis has an associated error process which provides a measure of how well the input processes can be used to predict the output processes and is also used to specify how the interactions between the recorded processes contribute to this error. Without assuming stationarity of the input, the time domain approach uses a sequence of probability models of increasing complexity in which the number of input processes to the model is progressively increased. This feature of the time domain approach was used to identify a preferred direction of interaction between the processes underlying the generation of the activity of the primary and secondary endings. In the presence of fusimotor activity and dynamic length changes imposed on the muscle, it was shown that the activity of the primary and secondary endings carried different information about the effects of the inputs imposed on the muscle spindle. The results presented in this work emphasise that the analysis of the behaviour of complex systems benefits from a combination of frequency and time domain methods

Generalized partially linear models on Riemannian manifolds

We introduce generalized partially linear models with covariates on Riemannian manifolds. These models, like ordinary generalized linear models, are a generalization of partially linear models on Riemannian manifolds that allow for scalar response variables with error distribution models other than a normal distribution. Partially linear models are particularly useful when some of the covariates of the model are elements of a Riemannian manifold, because the curvature of these spaces makes it difficult to define parametric models. The model was developed to address an interesting application: the prediction of children's garment fit based on three‐dimensional scanning of their bodies. For this reason, we focus on logistic and ordinal models and on the important and difficult case where the Riemannian manifold is the three‐dimensional case of Kendall's shape space. An experimental study with a well‐known three‐dimensional database is carried out to check the goodness of the procedure. Finally, it is applied to a three‐dimensional database obtained from an anthropometric survey of the Spanish child population. A comparative study with related techniques is carried out

Phase equilibria and glass transition in colloidal systems with short-ranged attractive interactions. Application to protein crystallization

We have studied a model of a complex fluid consisting of particles interacting through a hard core and a short range attractive potential of both Yukawa and square-well form. Using a hybrid method, including a self-consistent and quite accurate approximation for the liquid integral equation in the case of the Yukawa fluid, perturbation theory to evaluate the crystal free energies, and mode-coupling theory of the glass transition, we determine both the equilibrium phase diagram of the system and the lines of equilibrium between the supercooled fluid and the glass phases. For these potentials, we study the phase diagrams for different values of the potential range, the ratio of the range of the interaction to the diameter of the repulsive core being the main control parameter. Our arguments are relevant to a variety of systems, from dense colloidal systems with depletion forces, through particle gels, nano-particle aggregation, and globular protein crystallization.Comment: 20 pages, 10 figure

Differential expression analysis for sequence count data

*Motivation:* High-throughput nucleotide sequencing provides quantitative readouts in assays for RNA expression (RNA-Seq), protein-DNA binding (ChIP-Seq) or cell counting (barcode sequencing). Statistical inference of differential signal in such data requires estimation of their variability throughout the dynamic range. When the number of replicates is small, error modelling is needed to achieve statistical power.

*Results:* We propose an error model that uses the negative binomial distribution, with variance and mean linked by local regression, to model the null distribution of the count data. The method controls type-I error and provides good detection power. 

*Availability:* A free open-source R software package, _DESeq_, is available from the Bioconductor project and from "http://www-huber.embl.de/users/anders/DESeq":http://www-huber.embl.de/users/anders/DESeq