1,721,135 research outputs found
Multilevel models for analyzing people’s daily moving behavior
No full textA survey on the daily movement behavior of the people residing in the territory of the Municipality of Pisa, Italy, was carried out in October 2002. This work is aimed at modeling the distance covered and the number of trips taken in a day as functions of several individual characteristics. In order to take the potential intra-family and intra-area correlation of the observations into account, multilevel models are estimated. We use two and three level hierarchical linear and Poisson models to estimate the number of daily trips taken by an individual. Likelihood ratio tests indicate the movement behavior in 1 day is more alike for individuals within a family than for individuals from different families
Modeling sign concordance of quantile regression residuals with multiple outcomes
Full text linkQuantile regression permits describing how quantiles of a scalar response vari-
able depend on a set of predictors. Because a unique de nition of multivariate
quantiles is lacking, extending quantile regression to multivariate responses is
somewhat complicated. In this paper, we describe a simple approach based on
a two-step procedure: in the rst step, quantile regression is applied to each re-
sponse separately; in the second step, the joint distribution of the signs of the
residuals is modeled through multinomial regression. The described approach
does not require a multidimensional de nition of quantiles, and can be used to
capture important features of a multivariate response and assess the e ects of co-
variates on the correlation structure. We apply the proposed method to analyze
two di erent datasets
Logistic quantile regression to model cognitive impairment in Sardinian cancer patients
No full textWhen analyzing outcome variables that take on values within a finite bounded interval, standard analyses are often inappropriate. The conditional distribution of bounded outcomes given covariates is often asymmetric and bimodal (e.g., J- or U-shaped) and may substantially vary across covariate patterns. Analyzing this type of outcomes calls for specific methods that can constrain inference within the feasible range. The conditional mean is generally not an effective summary measure of a bounded outcome, and conditional quantiles are preferable. In this chapter we present an application of logistic quantile regression to model the relationship between Mini Mental State Examination (MMSE), a cognitive impairment score bounded between 0 and 30, with age and the results of a biochemical analysis (Oil Red O) for the determination of cytoplasmic neutral lipids in peripheral blood mononuclear cells in a sample of 124 cancer patients living in Sardinia, Italy. In addition we discuss an internal cross-validation method to optimally select the boundary correction in the logit transform
Quantile Regression Coefficients Modeling: a Penalized Approach
No full textModeling quantile regression coefficients functions permits describing the
coefficients of a quantile regression model as parametric functions of the order of the
quantile. This approach has numerous advantages over standard quantile regression,
in which different quantiles are estimated one at the time: it facilitates estimation and
inference, improves the interpretation of the results, and is statistically efficient. On
the other hand, it poses new challenges in terms of model selection. We describe a
penalized approach that can be used to identify a parsimonious model that can fit
the data well. We describe the method, and analyze the dataset that motivated the
present paper. The proposed approach is implemented in the qrcmNP package in R
A penalized approach to covariate selection through quantile regression coefficient models
No full textThe coefficients of a quantile regression model are one-to-one functions of the order of the quantile. In standard quantile regression (QR), different quantiles are estimated one at a time. Another possibility is to model the coefficient functions parametrically, an approach that is referred to as quantile regression coefficients modeling (QRCM). Compared with standard QR, the QRCM approach facilitates estimation, inference and interpretation of the results, and generates more efficient estimators. We designed a penalized method that can address the selection of covariates in this particular modelling framework. Unlike standard penalized quantile regression estimators, in which model selection is quantile-specific, our approach permits using information on all quantiles simultaneously. We describe the estimator, provide simulation results and analyse the data that motivated the present article. The proposed approach is implemented in the qrcmNP package in R
Modeling sign concordance of quantile regression residuals with multiple outcomes
No full textQuantile regression permits describing how quantiles of a scalar response variable depend on a set of predictors. Because a unique definition of multivariate quantiles is lacking, extending quantile regression to multivariate responses is somewhat complicated. In this paper, we describe a simple approach based on a two-step procedure: in the first step, quantile regression is applied to each response separately; in the second step, the joint distribution of the signs of the residuals is modeled through multinomial regression. The described approach does not require a multidimensional definition of quantiles, and can be used to capture important features of a multivariate response and assess the effects of covariates on the correlation structure. We apply the proposed method to analyze two different datasets
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
- …
