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Proxy (statistics)

About: Proxy (statistics) is a research topic. Over the lifetime, 5257 publications have been published within this topic receiving 94504 citations. The topic is also known as: proxy variable & proxy measurement.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors proposed a new specification for the empirical test based on the idea that in the previous models one crucial variable was missing: accounting policy choice, and they test their theoretical hypothesis using a sample of Spanish firms quoted on the Spanish continuous market from 1999 to 2002.
Abstract: The purpose of this paper is to provide some new evidence on the relationship between disclosure and the cost of equity capital. We propose a new specification for the empirical test based on the idea that in the previous models one crucial variable was missing: accounting policy choice. We test our theoretical hypothesis using a sample of Spanish firms quoted on the Spanish continuous market from 1999 to 2002. We adopt the ex-ante approach to measure the cost of equity capital, taking analysts predictions as a proxy for expected earnings. As an explanatory variable we use an index measuring annual report disclosure quality. This measure of disclosure is combined with a proxy for the accounting policy choice of the firm. We measure firms' conservatism using the modified Jones model of Dechow et al. (1995) to estimate discretionary accruals. Our results confirm that the relationship between disclosure and cost of capital is affected by the choice of accounting policy.

88 citations

Patent
Douglas G. Earl1, Cory R. Newey1
13 Feb 1998
TL;DR: In this article, the authors propose a system and method that provides services offered by proxy servers to client computers coupled to a network, where the services available to the client are dependent upon the topology of the network coupling the client to the proxy servers and access rights of the client with respect to the services.
Abstract: A system and method efficiently provides services offered by proxy servers to client computers coupled to a network. The system comprises a proxy server interface configured to expose the services within a web brower executing on a client computer. The services available to the client are dependent upon the topology of the network coupling the client to the proxy servers and the access rights of the client with respect to the services. The invention further provides an efficient method that allows clients to inherit functionality from the proxy servers as a function of the network topology and access rights.

88 citations

Proceedings ArticleDOI
02 May 2019
TL;DR: Focusing on three commonly used proxy diagnostic signals derived from social media, it is found that predictive models built on these data, although offer strong internal validity, suffer from poor external validity when tested on mental health patients.
Abstract: A growing body of research is combining social media data with machine learning to predict mental health states of individuals. An implication of this research lies in informing evidence-based diagnosis and treatment. However, obtaining clinically valid diagnostic information from sensitive patient populations is challenging. Consequently, researchers have operationalized characteristic online behaviors as "proxy diagnostic signals" for building these models. This paper posits a challenge in using these diagnostic signals, purported to support clinical decision-making. Focusing on three commonly used proxy diagnostic signals derived from social media, we find that predictive models built on these data, although offer strong internal validity, suffer from poor external validity when tested on mental health patients. A deeper dive reveals issues of population and sampling bias, as well as of uncertainty in construct validity inherent in these proxies. We discuss the methodological and clinical implications of these gaps and provide remedial guidelines for future research.

88 citations

Journal ArticleDOI
TL;DR: A new iterative-sampling-refinement algorithm is developed and implemented that is designed specifically to promote the accuracy of the SVR model for robust production optimization and compared with the popular stochastic simplex approximate gradient and reservoir-simulations runs.
Abstract: We design a new and general work flow for efficient estimation of the optimal well controls for the robust production-optimization problem using support-vector regression (SVR), where the cost function is the net present value (NPV). Given a set of simulation results, an SVR model is built as a proxy to approximate a reservoir-simulation model, and then the estimated optimal controls are found by maximizing NPV using the SVR proxy as the forward model. The gradient of the SVR model can be computed analytically so the steepest-ascent algorithm can easily and efficiently be applied to maximize NPV. Then, the well-control optimization is performed using an SVR model as the forward model with a steepest-ascent algorithm. To the best of our knowledge, this is the first SVR application to the optimal well-control problem. We provide insight and information on proper training of the SVR proxy for life-cycle production optimization. In particular, we develop and implement a new iterative-sampling-refinement algorithm that is designed specifically to promote the accuracy of the SVR model for robust production optimization. One key observation that is important for reservoir optimization is that SVR produces a high-fidelity model near an optimal point, but at points far away, we only need SVR to produce reasonable approximations of the predicting output from the reservoir-simulation model. Because running an SVR model is computationally more efficient than running a full-scale reservoir-simulation model, the large computational cost spent on multiple forward-reservoir-simulation runs for robust optimization is significantly reduced by applying the proposed method. We compare the performance of the proposed method using the SVR runs with the popular stochastic simplex approximate gradient (StoSAG) and reservoir-simulations runs for three synthetic examples, including one field-scale example. We also compare the optimization performance of our proposed method with that obtained from a linear-response-surface model and multiple SVR proxies that are built for each of the geological models.

87 citations

Journal ArticleDOI
TL;DR: In this paper, the authors analyzed the asset pricing and portfolio implications of an important barrier to sustainable investing: uncertainty about the corporate ESG profile and found that ESG uncertainty affects the risk-return trade-off, social impact, and economic welfare.

86 citations


Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20231,242
20222,473
2021334
2020262
2019250
2018282