1,720,971 research outputs found
Replication Data for: International Trends in Technological Progress: Evidence from Patent Citations, 1980-2011
No full textReplication data and Stata/GAUSS codes for "International Trends in Technological Progress: Evidence from Patent Citations, 1980-2011" (forthcoming in Economic Journal). Please read the readme file for details. KLL_Apr15.pdf is the pdf file of the paper - the appendix contains more information about the datasets. The datasets are essentially the up-to-date version of the NBER patent data (http://www.nber.org/patents/)
How Older Adults Use Online Videos for Learning
No full textOnline videos are a promising medium for older adults to learn. Yet, few studies have investigated what, how, and why they learn through online videos. In this study, we investigated older adults' motivation, watching patterns, and difficulties in using online videos for learning by (1) running interviews with 13 older adults and (2) analyzing large-scale video event logs (N=41.8M) from a Korean Massive Online Open Course (MOOC) platform. Our results show that older adults (1) are motivated to learn practical topics, leading to less consumption of STEM domains than non-older adults, (2) watch videos with less interaction and watch a larger portion of a single video compared to non-older adults, and (3) face various difficulties (e.g., inconvenience arisen due to their unfamiliarity with technologies) that limit their learning through online videos. Based on the findings, we propose design guidelines for online videos and platforms targeted to support older adults' learning
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
신뢰할 수 있는 대조 학습을 위한 정규화된 확률적 표현 방법
No full text학위논문(석사) - 한국과학기술원 : AI대학원, 2021.8,[iii, 22 p. :]Contrastive learning, which has recently received a lot of attention, exploits multi-views of instances (images) to learn view-invariant representation. Conventionally, the multi-views are generated by composition of multiple stochastic augmentations. Since the contrastive learning methods so far implicitly assumed that the generated multi-views are always an appropriate positive pair, their representations were made to be recklessly close in the representation space. However, in the case of complex images, which is common in real-world, inappropriate view pairs are likely to be generated. It likely results in learning problematic representation by encouraging representations to be closer even though the pair is not appropriate. To alleviate this problem, we propose to use a regularized stochastic representation considering the adequacy of the given view pair. With this, we devised a novel method that the model can attenuate the effect from inappropriate pairs. The proposed method consistently outperforms baselines for various downstream tasks (image classification, object detection) on various benchmark datasets (CIFAR-100, ImageNet-100, COCO).한국과학기술원 :AI대학원
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe 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
Essays on Robust Methods in Econometrics
Get PDFThis dissertation presents four essays on robust methods in econometrics. The first chapter, Optimal Shrinkage Estimation of Fixed Effects in Linear Panel Data Models, proposes a shrinkage estimator for the fixed effects in linear panel data models whose risk properties are robust against violations of the distributional assumptions that are commonly imposed. Shrinkage methods are frequently used to estimate fixed effects. However, the risk properties of existing estimators are fragile to violations of the underlying distributional assumptions. I develop an estimator for the fixed effects that obtains the best possible mean squared error (MSE) within a class of shrinkage estimators. This class includes conventional estimators, and the optimality does not require distributional assumptions. Importantly, the fixed effects are allowed to vary with time and to be serially correlated, and the shrinkage optimally incorporates the underlying correlation structure in this case. In such a context, I also provide a method to forecast fixed effects one period ahead. A simulation study shows that the proposed estimator substantially reduces the MSE relative to conventional methods when the distributional assumptions of the conventional methods are violated, and loses very little when the assumptions are met. Using administrative data on the public schools of New York City, I estimate a teacher value-added model and show that the proposed estimator makes an empirically relevant difference. In the second chapter, Inference in Moment Inequality Models That Is Robust to Spurious Precision under Model Misspecification\u27 (with Donald W.K. Andrews), we propose an inference procedure for the moment inequality model that is robust to misspecification in a specific sense. Standard tests and confidence sets in the moment inequality literature are not robust to model misspecification in the sense that they exhibit spurious precision when the identified set is empty. This paper introduces tests and confidence sets that provide correct asymptotic inference for a pseudo-true parameter in such scenarios, and hence, do not suffer from spurious precision. The pseudo-true parameter is defined as the parameter value that satisfies the minimally relaxed moment inequalities. The last two chapters are on the problem of constructing confidence intervals (CIs) under nonparametric settings. The provided CIs are robust in the sense that they account for (worst-case) finite sample bias and thus have uniform coverage over the underlying parameter space. In the third chapter, Inference in Regression Discontinuity Designs under Monotonicity (with Koohyun Kwon), we provide an inference procedure for the sharp regression discontinuity design (RDD) under monotonicity. Specifically, we consider the case where the true regression function is monotone with respect to (all or some of) the running variables and assumed to lie in a Lipschitz smoothness class. Such a monotonicity condition is natural in many empirical contexts, and the Lipschitz constant has an intuitive interpretation. We propose a minimax two-sided confidence interval (CI) and an adaptive one-sided CI. For the two-sided CI, the researcher is required to choose a Lipschitz constant where she believes the true regression function to lie in. This is the only tuning parameter, and the resulting CI has uniform coverage and obtains the minimax optimal length. The one-sided CI can be constructed to maintain coverage over all monotone functions, providing maximum credibility in terms of the choice of the Lipschitz constant. Moreover, the monotonicity makes it possible for the (excess) length of the CI to adapt to the true Lipschitz constant of the unknown regression function. Overall, the proposed procedures make it easy to see under what conditions on the underlying regression function the given estimates are significant, which can add more transparency to research using RDD methods. In the fourth chapter, Adaptive Inference in Multivariate Nonparametric Regression Models Under Monotonicity\u27\u27 (with Koohyun Kwon), we consider the problem of adaptive inference on a regression function at a point under a multivariate nonparametric regression setting. The regression function belongs to a Hölder class and is assumed to be monotone with respect to some or all of the arguments. We derive the minimax rate of convergence for CIs that adapt to the underlying smoothness, and provide an adaptive inference procedure that obtains this minimax rate. The procedure differs from that of Cai and Low (2004), intended to yield shorter CIs under practically relevant specifications. The proposed method applies to general linear functionals of the regression function, and is shown to have favorable performance compared to existing inference procedures
Optimal Shrinkage Estimation of Fixed Effects in Linear Panel Data Models
Get PDFShrinkage methods are frequently used to estimate fixed effects to reduce the
noisiness of the least squares estimators. However, widely used shrinkage
estimators guarantee such noise reduction only under strong distributional
assumptions. I develop an estimator for the fixed effects that obtains the best
possible mean squared error within a class of shrinkage estimators. This class
includes conventional shrinkage estimators and the optimality does not require
distributional assumptions. The estimator has an intuitive form and is easy to
implement. Moreover, the fixed effects are allowed to vary with time and to be
serially correlated, and the shrinkage optimally incorporates the underlying
correlation structure in this case. In such a context, I also provide a method
to forecast fixed effects one period ahead
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe 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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