1,720,983 research outputs found
Estimation under mode effects and proxy surveys, accounting for non-ignorable nonresponse
No full textWe propose a new, model-based methodology to address two major problems in survey sampling: The first problem is known as mode effects, under which responses of sampled units possibly depend on the mode of response, whether by internet, telephone, personal interview, etc. The second problem is of proxy surveys, whereby sampled units respond not only about themselves but also for other sampled. For example, in many familiar household surveys, one member of the household provides information for all other members, possibly with measurement effects. Ignoring the existence of mode effects and/or possible measurement effects in proxy surveys could result in possible bias in point estimators and subsequent inference. Our approach accounts also for nonignorable nonresponse. We illustrate the proposed methodology by use of simulation experiments and real sample data, with known true population values
Mean square error estimation of small area predictors by use of parametric and nonparametric bootstrap
No full textIn this article, we propose and compare some old and new parametric andnonparametric bootstrap methods for MSE estimation in small area estimation, restricting to the case of the widely used Fay-Herriot model. The parametric method consists of generating parametrically a large number of area bootstrap samples from the model fitted to the original data, re-estimating the model parameters for each bootstrap sample and then estimating the separate components of the MSE. The use of double-bootstrap is also considered. The nonparametric method generates the samples by bootstrapping standardized residuals, estimated from the original sample data. The bootstrap procedures are compared to other methods proposed in the literature in a simulation study, which also examines the robustness of the various methods to non-normality of the model error terms. A design-based MSE estimator for the Fay-Herriot model-dependent predictor is also described and its performance is investigated in a separate simulation study
Statistical inference under nonignorable sampling and nonresponse - an empirical likelihood approach
No full textStatistical models are often based on sample surveys. When the sample selection probabilities and/or the response probabilities are related to a model outcome variable, even after conditioning on the model covariates, the model holding for the observed data is different from the model holding in the population, resulting in biased inference if not accounted for properly. Accounting for sample selection bias is relatively simple because the sample selection probabilities are usually known. Accounting for nonignorable nonresponse is much harder since the response probabilities are, in practice, unknown. In this article, we develop a new approach for modelling complex survey data, which accounts simultaneously for nonignorable sampling and nonresponse. Our proposed approach combines the nonparametric empirical likelihood with a parametric model for the response probabilities, which contains the outcome variable as one of the covariates. Combining the model holding for the responding units with the model for the response probabilities enables extracting the model holding for the missing data and imputing them. We propose ways of testing the underlying model holding for the respondents’ data. Simulation results illustrate the good performance of the approach in terms of parameter estimation and imputation. We conclude with an application to the household expenditure survey in Israel, carried out by Israel’s Central Bureau of Statistics. The survey collects information on the socio-demographic characteristics of each member of the sampled households (HH), as well as detailed information on the HH income and expenditure. The total sample size was n = 12,136 with 7,827 responding HHs. The target estimated parameter in this application is the population mean of the gross HH income
Least squares estimation for GARCH (1,1) model with heavy tailed errors
No full textGARCH (1,1) models are widely used for modelling processes with time varying volatility. These include financial time series, which can be particularly heavy tailed. In this paper, we propose a novel log-transform-based least squares approach to the estimation of GARCH(1,1) models. Within this approach the scale of the estimated
volatility is dependent on an unknown tuning constant. By means of a backtesting exercise on both real and simulated
data we show that knowledge of the tuning constant is not crucial for Value at Risk prediction. However, this does not
apply to many other applications where correct identification of the volatility scale is required. In order to overcome
this difficulty, we propose two alternative two-stage least squares estimators (LSE) and derive their asymptotic properties under very mild moment conditions for the errors. In particular, we establish the consistency and asymptotic normality at the standard convergence rate of sqrt-n for our estimators. Their finite sample properties are assessed by means of an extensive simulation study
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
Least squares estimation for GARCH (1,1) model with heavy tailed errors
No full textGARCH (1,1) models are widely used for modelling processes with time varying volatility. These include financial time series, which can be particularly heavy tailed. In this paper, we propose a novel log-transform-based least squares approach to the estimation of GARCH (1,1) models. Within this approach the scale of the estimated volatility is dependent on an unknown tuning constant. By means of a backtesting exercise on both real and simulated data we show that knowledge of the tuning constant is not crucial for Value at Risk prediction. However, this does not apply to many other applications where correct identification of the volatility scale is required. In order to overcome this difficulty, we propose two alternative two-stage least squares estimators (LSE) and derive their asymptotic properties under very mild moment conditions for the errors. In particular, we establish the consistency and asymptotic normality at the standard convergence rate of "\sqrt n" for our estimators. Their finite sample properties are assessed by means of an extensive simulation study
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
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