1,327 research outputs found
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
Music therapy for supporting informal carers of adults with life-threatening illness pre- and post-bereavement; a mixed-methods systematic review
Funding Information: This systematic review is funded by the Music Therapy Charity Scoping Project Competition (UK), grant funding awarded from 2021–2022. The funder had no role in the design of the study, data collection, analysis, and interpretation of data and in writing the manuscript.Peer reviewe
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
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