1,721,259 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
Small area estimation of survey weighted counts under aggregated level spatial model
Full text linkThe empirical predictor under an area level version of the generalized linear mixed model (GLMM) is extensively used in small area estimation (SAE) for counts. However, this approach does not use the sampling weights or clustering information that are essential for valid inference given the informative samples produced by modern complex survey designs. This paper describes an SAE method that incorporates this sampling information when estimating small area proportions or counts under an area level version of the GLMM. The approach is further extended under a spatial dependent version of the GLMM (SGLMM). The mean squared error (MSE) estimation for this method is also discussed. This SAE method is then applied to estimate the extent of household poverty in different districts of the rural part of the state of Uttar Pradesh in India by linking data from the 2011-12 Household Consumer Expenditure Survey collected by the National Sample Survey Office (NSSO) of India, and the 2011 Indian Population Census. Results from this application indicate a substantial gain in precision for the new methods compared to the direct survey estimates
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
Estimation of District level poor household in the state of Uttar Pradesh in India by combining NSSO Survey and Census Data
No full textTThe National Sample Survey Organisation (NSSO) surveys are the main source of official statistics in India. A range of invaluable data at the macro level (e.g. state and national level) is generated through these surveys. However, the NSSO data cannot be used directly to produce reliable estimates at the micro level (e.g. district or further disaggregate level) due to small sample sizes. There is a rapidly growing demand of such micro level statistics in India as the country is moving from centralized to more decentralized planning system. In this article we employ small area estimation (SAE) techniques to derive model- based estimates of proportion of poor households at district level in the State of Uttar Pradesh in India by linking data from the Household Consumer Expenditure Survey 2006-07 of NSSO 63rd round and the Population Census. The poverty line used in this study is same as those of year 2004-05, given by Planning Commission, Govt of India. The poverty line is used to identify whether a given household is poor or not. A household having monthly per capita consumer expenditure below the states poverty line is categorised as poor household. The results show that the model-based estimates are reliable. In contrast, the direct estimates are very unstable. These estimates are expected to provide invaluable information to policy-analysts and decision-makers
A Spatially Nonstationary Fay-Herriot Model for Small Area Estimation
No full textSmall area estimates based on the widely-used area-level model proposed in Fay and Herriot (1979) assume that the area level direct estimates are spatially uncorrelated. In many cases, however, this is not the case. Extensions of the Fay-Herriot model to allow for spatial correlation have been proposed, but all assume spatial stationarity, i.e. the parameters of the associated regression model for the small area characteristic of interest do not vary spatially. Instead, spatial effects are introduced by imposing a spatial correlation structure on the regression errors. In this paper we propose an extension to the Fay-Herriot model that accounts for the presence of spatial nonstationarity, i.e. where the parameters of this regression model vary spatially. We refer to the predictor based on this extended model as the nonstationary empirical best linear unbiased predictor (NSEBLUP). We also develop two different estimators for the mean squared error of the NSEBLUP. The first estimator uses approximations similar to those in Opsomer et al. (2008). The second estimator is based on the parametric bootstrapping approach of Gonzalez-Manteiga et al. (2008) and Molina et al. (2009). Results from model-based and design-based simulation studies using spatially nonstationary data indicate that the NSEBLUP compares favourably with alternative area-level predictors that ignore this spatial nonstationarity. In addition, both proposed methods for estimating its mean squared error seem adequate
Model Based Direct Estimation of Small Area Distributions
No full textMuch of the small area estimation literature focuses on population totals and means. However, users of survey data are often interested in the finite population distribution of a survey variable, and the measures (e.g. medians, quartiles, percentiles) that characterise the shape of this distribution at small area level. In this paper we propose a model-based direct estimator (MBDE, see Chandra and Chambers, 2009) of the small area distribution function. The MBDE is defined as weighted sum of sample data from the area of interest, with weights derived from the calibrated spline-based estimate of the finite population distribution function introduced by Harms and Duchesne (2006), under an appropriately specified regression model with random area effects. We also discuss the mean squared error estimation of the MBDE. Monte Carlo simulations based on both simulated and real datasets show that the proposed MBDE and its associated mean squared error estimator perform well when compared with alternative estimators of the area-specific finite population distribution function
- …
