1,721,365 research outputs found
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
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
ORDINAL CLASSIFICATION TREES: METHODS AND APPLICATION
Full text linkDuring my PhD in Biomedical Statistics, I focused my research on the study of ordinal classification tree methodology, to be applied in case of ordinal categorical outcome, and all the statistical and computational methods related to them. Then, I provided an application on the set of controls of Italian case-control studies, in order to identify the profiles of individuals according to their total energy intake, and to their consumption of red meat and processed meat.
Introduction to classification trees
Tree-based methods are non parametric regression methods belonging to a group of techniques called “recursive partitioning”. They recursively partition the feature space, which includes all the predictors, into a set of nested rectangular areas. The main objective of this technique is to obtain subgroups of observations (nodes) which should be more homogeneous as possible in terms of the values of the response variable. A quantitative measure of the extent of a node homogeneity is the notion of node purity, with a completely pure node having all the observations in it belong to the same category of the outcome. When the response to be predicted is ordinal, an ordinal impurity measure is usually preferred. The most frequently used ordinal impurity function is the generalized Gini function. The split, among all the possible binary splits for a given node, resulting in the largest value of the decrease in node impurity, is selected.
The process of splitting continues in each node until some stop condition is reached, and a large tree T0 is built. However, a very large tree may overfit data. Overfitting refers to the fact that a classifier adapts too closely to the training dataset, leading to poor test performance when applied to the validation set. Thus, a common strategy is to eliminate those parts of a classifier that are likely to overfit the training data. This process is called pruning, and consists in eliminating branches that do not add information to prediction accuracy. Classification tree analysis uses a cost-complexity pruning. This approach balances the complexity (i.e., the number of predictors and terminal nodes) of the sub-tree and the overall misclassification rate.
Two predictive performance measures to be used when Y is an ordinal outcome are the total number of misclassified observations R_mr (T) and the total misclassification cost R_mc (T). With the first one, the class of assignment for observations within each node is usually the modal class of the outcome, while with the second one it is the median class.
With classification trees we are able to examine complex interactions among risk factors that do not need to be pre-specified a priori. Moreover, we will likely be able to identify the most important risk (or protective) factors among various predictors, and we have the possibility to identify ideal cut-offs of continuous variables, according to some pre-specified criteria. On the other hand, since this is a data-driven method, a drawback is that small changes in the data can result in a very different series of splits, making interpretation instable.
Application to real data
To conduct the analyses on ordinal classification tree method, I used the set of controls (n=7750) of various case-control studies conducted in six Italian provinces between 1991 and 2008. Controls were individuals with no history of cancer admitted to the same hospitals of cases for acute, non-neoplastic conditions, unrelated to diseases or to conditions linked to the cancer in study. Predictors were food groups, related to the subjects' dietary habits during the 2 years before hospitalization, assessed through a validated and reproducible food frequency questionnaire, which included information on weekly consumption of 78 foods and beverages.
Two different types of analyses were performed to evaluate the performance of classification trees methodology in predicting the category of total energy intake (kcal/day): single tree analysis and resampling analysis (B=100, to overcome sampling variability). I compared five different scenarios, four in the context of ordinal classification trees (generalized Gini impurity function) and one in the context of nominal classification trees (Gini impurity measure). In the ordinal context, each scenario was a combination of the splitting function (absolute or quadratic misclassification cost) and the predictive performance measure (misclassification error rate/mode or misclassification cost/median). Also classification trees to predict the daily consumption (grams) of red meat and processed meat was performed.
Results and Discussion
The most important predictor for energy intake was bread consumption. Indeed, this predictor resulted as the first split in each of the five scenarios, with a threshold of 16.4 portions/week. Other predictors common to all the five scenarios were desserts and red meat intake.
The comparison between five different methods put in evidence that, in case of ordinal outcome, adequate ordinal methods should be preferred. According to the prediction accuracy between various ordinal models, it emerged that models with quadratic misclassification cost had better predictive power, in particular when median was used to assign outcome classes. A good predictive performance was also observed with quadratic misclassification cost and modal values. This findings were consistent both in the single-tree and in the resampling analysis. In the single-tree analysis, the values of Somers’ d measure ranged between 0.489 and 0.534. In the resampling analysis, Friedman’s test rejected the global equality hypothesis across the five models (p<0.001).
In the application on red meat and processed meat intake, it emerged that important predictors for red meat consumption were total intake of sweets (first split) and bread consumption, while important predictors for processed meat intake were the consumption of eggs, bread and sweets. In particular, subjects eating less than 1 egg per week and less than 2 portions of bread per day were classified as having small consumption (<25 g/day) of processed meat. On the other hand, individuals eating more than 1 egg per week and more than 30 portions of sweets per week were predicted to have a great (≥50 g/day) consumption of processed meat.
Possible future researches should try to take advantage of findings obtained with classification tree methodologies in order to investigate the relationship between red meat and processed meat intake and the risk of colorectal cancer and the risk of other neoplasms. Moreover, the application of recursive partitioning techniques in predictive settings, including data on cancer screening, may be of interest for future researches
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
A different class-assignment rule to build classification trees for ordinal outcomes
No full textIntroduction. Classification and regression trees (CART) are binary recursive partitioning methods designed to construct prediction models for categorical (classification) or continuous (regression) variables from data. One of the key elements of classification trees is the assignment rule of each terminal node (leaf) to a class outcome.
Objectives. Evaluating the performance of the ‘median trees’, built with a novel approach to class assignment, compared to the ‘modal trees’, built with the majority rule, when an ordinal outcome (y) is assumed.
Materials and Methods. Modal trees were estimated using the modal class among the observations that fall into each leaf to assign y-classes, whereas median trees were estimated through the median class. According to the assignment rule adopted, the predicted power of the trees was evaluated by two different approaches: modal trees minimized the total number of errors; median trees minimized the sum of absolute distances between predicted class and observed class. Tree performances were evaluated through the gamma statistic, measuring the association between observed and predicted classes. Three real datasets with different number of y-levels (from four to six) were analyzed. Each dataset was divided into a training set, for building the trees, and a testing set, for evaluating prediction accuracy. A resampling of the testing set (n=30) was carried out to derive robust estimates. Binomial test and paired t-test were used to compare the significance of differences between tree performances.
Results. Median tree performances were significantly better than modal ones with five and six y-classes. Significant differences were not observed with four levels of the outcome. No matter of the number of y-classes, median trees showed a simpler structure (smaller number of leaves) than modal ones.
Conclusion. Median trees showed a better performance than modal trees with an increasing number of y-levels and generally provided a simpler structure which allows an easier interpretation of the patterns and connections among groups of interest
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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