1,721,023 research outputs found
Multiscale Bernstein polynomials for densities
Full text linkOur focus is on constructing a multiscale nonparametric prior for densities. The Bayes density estimation literature is dominated by single scale methods, with the exception of Polya trees, which favor overly-spiky densities even when the truth is smooth. We propose a multiscale Bernstein polynomial family of priors, which produce smooth realizations that do not rely on hard partitioning of the support. At each level in an infinitely-deep binary tree, we place a beta dictionary density; within a scale the densities are equivalent to Bernstein polynomials. Using a stick-breaking characterization, stochastically decreasing weights are allocated to the finer scale dictionary elements. A slice sampler is used for posterior computation, and properties are described. The method characterizes densities with locally-varying smoothness, and can produce a sequence of coarse to fine density estimates. An extension for Bayesian testing of group differences is introduced and applied to DNA methylat..
Bayesian hierarchical functional data analysis via contaminated informative priors.
No full textA variety of flexible approaches have been proposed for functional data analysis, allowing both
the mean curve and the distribution about the mean to be unknown. Such methods are most useful when
there is limited prior information. Motivated by applications to modeling of temperature curves in the menstrual
cycle, this article proposes a flexible approach for incorporating prior information in semiparametric
Bayesian analyses of hierarchical functional data. The proposed approach is based on specifying the distribution
of functions as a mixture of a parametric hierarchical model and a nonparametric contamination.
The parametric component is chosen based on prior knowledge, while the contamination is characterized as
a functional Dirichlet process. In the motivating application, the contamination component allows unanticipated
curve shapes in unhealthy menstrual cycles. Methods are developed for posterior computation, and
the approach is applied to data from a European fecundability study
Cervical mucus secretions on the day of intercourse: An accurate marker of highly fertile days
No full textObjective: To provide estimates of the probabilities of conception according to vulvar mucus observations classified by the woman on the day of intercourse.
Study design: Prospective cohort study of 193 outwardly healthy Italian women using the Billings Ovulation Method. Outcome measures include 161 conception cycles and 2594 non-conception cycles with daily records of the type of mucus and the occurrences of sexual intercourse.
Results: The probability of conception ranged from 0.003 for days with no noticeable secretions to 0.29 for days with most fertile-type mucus detected by the woman. The probability of most fertile type mucus by day of the menstrual cycle increased from values <20% outside of days 10–17 to a peak of 59% on day 13.
Conclusion: Regardless of the timing of intercourse in the menstrual cycle, the probability of conception is essentially 0 on days with no secretions. This probability increases dramatically to near 30% on days with most fertile-type mucus, an association that accurately predicts both the timing of the fertile interval and the day-specific conception probabilities across the menstrual cycle
A Bayesian oblique factor model with extension to tensor data
No full textIn this short paper, we discuss a novel way of constructing prior distributions for correlation matrices and an associated approach to inference. We construct a prior penalizing large correlations, which we incorporate into an oblique factor model and a Candecomp/Parafac model for three-way data. We argue that this choice of prior for the factor correlation matrix, combined with a shrinkage prior for elements of the factor loadings matrix, leads to interpretable solutions. At the meeting we will demonstrate this through applications to real data
Generalized infinite factorization models
Full text linkFactorization models express a statistical object of interest in terms of a
collection of simpler objects. For example, a matrix or tensor can be expressed
as a sum of rank-one components. However, in practice, it can be challenging to
infer the relative impact of the different components as well as the number of
components. A popular idea is to include infinitely many components having
impact decreasing with the component index. This article is motivated by two
limitations of existing methods: (1) lack of careful consideration of the
within component sparsity structure; and (2) no accommodation for grouped
variables and other non-exchangeable structures. We propose a general class of
infinite factorization models that address these limitations. Theoretical
support is provided, practical gains are shown in simulation studies, and an
ecology application focusing on modelling bird species occurrence is discussed
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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