Relevant bibliographies by topics / Estimating value

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Estimating value'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Estimating value"

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Carvalho, Mamede de. "Estimating the value of estimation." Clinical Neurophysiology 123, no. 10 (October 2012): 1904–5. http://dx.doi.org/10.1016/j.clinph.2012.03.003.

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Canıkalp, Ebru, İlter Ünlükaplan, and Muhammed Çelik. "Estimating Value Added Tax Gap in Turkey." INTERNATIONAL JOURNAL OF INNOVATION AND ECONOMIC DEVELOPMENT 2, no. 3 (2015): 18–25. http://dx.doi.org/10.18775/ijied.1849-7551-7020.2015.23.2002.

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As an important issue in the fiscal structure of a country, tax gap is defined as the difference between tax burden that the taxpayer should face and the amount actually paid. In this study, tax gap was evaluated by the framework of the Value Added Tax. The reason behind this choice, i.e. Value Added Tax Gap (VAT Gap) is to make an effort to evaluate the efficiency of the tax administration, the compliance of the taxpayers and the relationship between policy gap and the compliance gap. With this aim, VAT Gap and the various methods to calculate this gap were examined. Furthermore, based on the reports by the European Commission, VAT Gap in Turkey for 1993-2014 period were estimated and evaluated by employing the top-down method.
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Fredlund, Delwyn G., Daichao Sheng, and Jidong Zhao. "Estimation of soil suction from the soil-water characteristic curve." Canadian Geotechnical Journal 48, no. 2 (February 2011): 186–98. http://dx.doi.org/10.1139/t10-060.

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Soil-water characteristic curves (SWCCs) are routinely used for the estimation of unsaturated soil property functions (e.g., permeability functions, water storage functions, shear strength functions, and thermal property functions). This paper examines the possibility of using the SWCC for the estimation of in situ soil suction. The paper focuses on the limitations of estimating soil suctions from the SWCC and also suggests a context under which soil suction estimations should be used. The potential range of estimated suction values is known to be large because of hysteresis between drying and wetting SWCCs. For this, and other reasons, the estimation of in situ suctions from the SWCC has been discouraged. However, a framework is suggested in this paper for estimating the median value for in situ soil suction along with a likely range of soil suction values (i.e., maximum and minimum values). The percentage error in the estimation of soil suction from the SWCC is shown to be lowest for sand soils and highest for clay soils.
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COLBORN, ASLI F. "Estimating A Firm's Continuing Value." Business Valuation Review 10, no. 4 (December 1991): 157–64. http://dx.doi.org/10.5791/0882-2875-10.4.157.

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Korteweg, Arthur, and Morten Sorensen. "Estimating Loan-to-Value Distributions." Real Estate Economics 44, no. 1 (March 24, 2015): 41–86. http://dx.doi.org/10.1111/1540-6229.12086.

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Russell, K. G. "Estimating the Value ofeby Simulation." American Statistician 45, no. 1 (February 1991): 66–68. http://dx.doi.org/10.1080/00031305.1991.10475769.

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Kadan, Ohad, and Asaf Manela. "Estimating the Value of Information." Review of Financial Studies 32, no. 3 (August 10, 2018): 951–91. http://dx.doi.org/10.1093/rfs/hhy087.

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Miao, Chuanhong, Xican Li, and Jiehui Lu. "Soil pH value grey relation estimation model based on hyper-spectral." Grey Systems: Theory and Application 8, no. 4 (October 8, 2018): 436–47. http://dx.doi.org/10.1108/gs-05-2018-0027.

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PurposeThe purpose of this paper is to establish the grey relational estimating model of soil pH value based on hyper-spectral data.Design/methodology/approachAs to the uncertainty of the factors affecting the soil pH value estimation based on hyper-spectral, the grey weighted relation estimation model was set up according to the grey system theory. Then the linear regression correction model is established according to the difference and grey relation degree information between the estimated samples and their corresponding pattern. At the same time, the model was applied to Hengshan county of Shanxi province.FindingsThe results are convincing: not only that the linear regression correction model of grey relation estimating pattern of soil pH value based on hyper-spectral data is valid, but also the model’s estimating accuracy is higher, which the corrected average relative error is 0.2578 per cent, and the decision coefficientR2=0.9876.Practical implicationsThe method proposed in the paper can be used at soil pH value hyper-spectral inversion and even for other similar forecast problem.Originality/valueThe paper succeeds in realising both the soil pH value hyper-spectral grey relation estimating pattern based on the grey relational theory and the correction model of the estimating pattern by using the linear regression.
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Elsinghorst, C., P. Groeneboom, P. Jonathan, L. Smulders, and P. H. Taylor. "Extreme Value Analysis of North Sea Storm Severity." Journal of Offshore Mechanics and Arctic Engineering 120, no. 3 (August 1, 1998): 177–83. http://dx.doi.org/10.1115/1.2829538.

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In this paper we consider the estimation of North Sea storm severity, for storms with return periods in the interval 100 to 500 yr. The analysis consists of: modeling the tail-distribution for a set of data for storm severity (using, e.g., storm hindcast data); estimating extreme storm severity; estimating confidence intervals for extreme storm severity; validating the bias and variance of estimates using simulation studies, for known underlying model forms; and estimating the robustness of extreme quantile estimates with respect to misspecification of the underlying model for the tail-distribution of storm severity. Applications to NESS (Northern European Hindcast Study) hindcast data at clusters of locations in the northern, central and southern North Sea are considered. Results suggest, in particular, the existence of a physical upper limit for storm severity in the North Sea and a close to constant value for the extreme value index, γ ≈ −0.2.
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Kountur, Ronny. "The likelihood value of residual risk estimation in the management of enterprise risk." Investment Management and Financial Innovations 15, no. 3 (July 13, 2018): 49–55. http://dx.doi.org/10.21511/imfi.15(3).2018.04.

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A model for estimating the likelihood value of residual risk (Y) is introduced. The model consists of three independent variables: the likelihood value of risk before risk treatment (X1), the quality of risk treatment (X2), and the appropriateness of risk treatment (X3). An experimental research design with a multiple linear regression analysis was used in the estimation. All independent variables, the likelihood value of risk before treatment, the quality of risk treatment, and the appropriateness of risk treatment, can be significantly used to estimate the likelihood value of residual risk. Since no model of estimating residual risk of likelihood had been introduced yet, the findings of this study provide significant contribution to firms or organizations that need to assess the likelihood value of residual risks.
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Dissertations / Theses on the topic "Estimating value"

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Yan, Yang. "Essays in modelling and estimating Value-at-Risk." Thesis, London School of Economics and Political Science (University of London), 2014. http://etheses.lse.ac.uk/1033/.

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The thesis concerns semiparametric modelling and forecasting Value-at-Risk models, and the applications of these in financial data. Two general classes of semiparametric VaR models are proposed, the first method is introduced by defining some efficient estimators of the risk measures in a semiparametric GARCH model through moment constraints and a quantile estimator based on inverting an empirical likelihood weighted distribution. It is found that the new quantile estimator is uniformly more efficient than the simple empirical quantile and a quantile estimator based on normalized residuals. At the same time, the efficiency gain in error quantile estimation hinges on the efficiency of estimators of the variance parameters. We show that the same conclusion applies to the estimation of conditional Expected Shortfall. The second model proposes a new method to forecast one-period-ahead Value-at-Risk (VaR) in general ARCH(1) models with possibly heavy-tailed errors. The proposed method is based on least square estimation for the log-transformed model. This method imposes weak moment conditions on the errors. The asymptotic distribution also accounts for the parameter uncertainty in volatility estimation. We test our models against some conventional VaR forecasting methods, and the results demonstrate that our models are among the best in forecasting VaR.
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Pagliari, Romano Italo. "Estimating the monetary value of airport runway departure slots." Thesis, Cranfield University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.245446.

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Norrman, Henrik. "Estimating p-values for outlier detection." Thesis, Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-25662.

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Outlier detection is useful in a vast numbers of different domains, wherever there is data and a need for analysis. The research area related to outlier detection is large and the number of available approaches is constantly growing. Most of the approaches produce a binary result: either outlier or not. In this work approaches that are able to detect outliers by producing a p-value estimate are investigated. Approaches that estimate p-values are interesting since it allows their results to easily be compared against each other, followed over time, or be used with a variable threshold. Four approaches are subjected to a variety of tests to attempt to measure their suitability when the data is distributed in a number of ways. The first approach, the R2S, is developed at Halmstad University. Based on finding the mid-point of the data. The second approach is based on one-class support vector machines (OCSVM). The third and fourth approaches are both based on conformal anomaly detection (CAD), but using different nonconformity measures (NCM). The Mahalanobis distance to the mean and a variation of k-NN are used as NCMs. The R2S and the CAD Mahalanobis are both good at estimating p-values from data generated by unimodal and symmetrical distributions. The CAD k-NN is good at estimating p-values when the data is generated by a bimodal or extremely asymmetric distribution. The OCSVM does not excel in any scenario, but produces good average results in most of the tests. The approaches are also subjected to real data, where they all produce comparable results.
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Vallaud, Thierry. "Estimating potential customer value using customer data : using a classification technique to determine customer value /." Abstract and full text available, 2009. http://149.152.10.1/record=b3077978~S16.

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Thesis (M.S.) -- Central Connecticut State University, 2009.
Thesis advisor: Daniel Larose. "... in partial fulfillment of the requirements for the degree of Master of Science in Data Mining." Includes bibliographical references (leaves 37-39). Also available via the World Wide Web.
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Klacar, Dorde. "Estimating Expected Exposure for the Credit Value Adjustment risk measure." Thesis, Umeå universitet, Nationalekonomi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-73104.

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Kwon, Youngho. "Estimating the value of financial transmission rights for transmission expansion." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.497600.

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The current situation of insufficient investment in transmission tends to weaken the advantages of a deregulated power industry. These shortages mainly arise because of the risk of not recovering the cost of investing in new lines Location Marginal prices (LMPs) and Financial Transmission Rights (FTRs) are key elements in reducing these investment risks. Market participants are able to hedge against price fluctuations caused by transmission congestion through the purchase or sale of FTRs. The value of FTRs, which is tied to the difference in prices between locations in the network, would indicate where transmission expansion should be implemented.
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Hu, Youxin. "Auction Behavior: Essays on Externalities and Estimating Value Distributions from EBay." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1250677684.

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Boateng, F. (Forster). "Estimating value at risk using extreme value theory:is the two dimensional inhomogeneous Poisson model better than the others." Master's thesis, University of Oulu, 2015. http://urn.fi/URN:NBN:fi:oulu-201510152084.

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This research presents an application of extreme value theory to estimate the value at risk of a market position particularly of the OMX Hex Index. There are many approaches to computing value at risk alongside the extreme value method. One fundamental problem the manager of risk face is “what is the optimal choice of value at risk estimator that will best predict the risk?” This implies that choosing an approach to best predict value at risk is challenging. This study proposed a method of estimating value at risk using the two-dimensional inhomogeneous Poisson model. An extreme value theory method that is based on the Peak Over Threshold (POT). The method takes into consideration time varying parameters through some explanatory variables. The study also shows how well theoretical model fit real financial data. The data used is the daily log return of the OMX Hex Index from the period 1990 to 2014. From the data we show empirically that the OMX Hex Index obeys a Fréchet distribution. The explanatory variables for the study are GARCH volatilities, annual trend and quarter dummies. The explanatory variables are all available at time t-1. With the help of the fitted models we adopt the two-dimensional inhomogeneous Poisson model approach to estimating value at risk over the two-dimensional homogeneous Poisson model and other classical or traditional methods, and find that this better predict value at risk estimates.
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Li, Qiuyuan Jimmy. "The value of field experiments in estimating demand elasticities and maximizing profit." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/91105.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 99-101).
In many situations, the capabilities of firms are better suited to conducting and analyzing field experiments than to analyzing sophisticated demand models. However, the practical value of using field experiments to optimize marketing decisions remains relatively unstudied. We investigate category pricing decisions that require estimating a large matrix of cross-product demand elasticities and ask: how many experiments are required as the number of products in the category grows? Our main result demonstrates that if the categories have a favorable structure, then we can learn faster and reduce the number of experiments that are required: the number of experiments required may grow just logarithmically with the number of products. These findings potentially have important implications for the application of field experiments. Firms may be able to obtain meaningful estimates using a practically feasible number of experiments, even in categories with a large number of products. We also provide a relatively simple mechanism that firms can use to evaluate whether a category has a structure that makes it feasible to use field experiments to set prices. We illustrate how to accomplish this using either a sample of historical data or a pilot set of experiments. Historical data often suffer from the problem of endogeneity bias, but we show that our estimation method is robust to the presence of endogeneity. Besides estimating demand elasticities, firms are also interested in using these elasticities to choose an optimal set of prices in order to maximize profits. We formulate the profit maximization problem and demonstrate that substantial profit gains can also be achieved using a relatively small number of experiments. In addition, we discuss how to evaluate whether field experiments can help optimize other marketing decisions, such as selecting which products to advertise or promote. We adapt our models and methodologies to this setting and show that the main result that relatively few experiments are needed to estimate elasticities and to increase profits continues to hold.
by Jimmy Qiuyuan Li.
Ph. D.
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ZHANG, XIAOYI. "ESTIMATING PEAKING FACTORS WITH POISSON RECTANGULAR PULSE MODEL AND EXTREME VALUE THEORY." University of Cincinnati / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1123875661.

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Books on the topic "Estimating value"

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Langmaid, John. Estimating: Getting value from function. Bracknell: BSRIA, 2003.

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Garrido, J. Roberto R. Estimating land value using the linear programming method. Manila]: Development Alternative, 1993.

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Noland, Rutgers University Robert B., and Abt Associates Stan Hsieh. Estimating the Value of Truck Travel Time Reliability. Washington, D.C.: Transportation Research Board, 2019. http://dx.doi.org/10.17226/25655.

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Lopez, J. Humberto. Estimating the value of travel time in San Sebastian (Spain). [s.l.]: typescript, 1991.

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Madden, John R. Estimating the value of Tasmanian national parks to park visitors. Gold Coast, Qld: CRC for Sustainable Tourism, 2002.

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Cannon, Michael G. Procedural guidelines for estimating residential and business structure value for use in flood damage estimations. Alexandria, Va: U.S. Army Corps of Engineers, Water Resources Support Center, Institute for Water Resources, 1995.

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Lucas, Deborah. Estimating the value of subsidies for federal loans and loan guarantees. [Washington, D.C.]: Congress of the United States, Congressional Budget Office, 2004.

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Lucas, Deborah. Estimating the value of subsidies for federal loans and loan guarantees. [Washington, D.C.]: Congress of the United States, Congressional Budget Office, 2004.

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Lucas, Deborah. Estimating the value of subsidies for federal loans and loan guarantees. [Washington, D.C.]: Congress of the United States, Congressional Budget Office, 2004.

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Lucas, Deborah. Estimating the value of subsidies for federal loans and loan guarantees. [Washington, D.C.]: Congress of the United States, Congressional Budget Office, 2004.

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Book chapters on the topic "Estimating value"

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Fukushima, Takuya, Taku Hasegawa, and Tomoharu Nakashima. "Estimating Optimal Values for Intentional-Value-Substitution Learning." In Modeling Decisions for Artificial Intelligence, 319–29. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26773-5_28.

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Schulenburg, Lennart. "Estimating Economic Value of NDE 4.0." In Handbook of Nondestructive Evaluation 4.0, 1–31. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-48200-8_54-1.

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Khan, M. Rashid, Matthew A. McMahon, and S. J. DeCanio. "Estimating the Heating Value of Sewage Sludge." In Clean Energy from Waste and Coal, 144–56. Washington, DC: American Chemical Society, 1992. http://dx.doi.org/10.1021/bk-1992-0515.ch012.

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Locarek-Junge, Hermann, and Ralf Prinzler. "Estimating Value-at-Risk Using Neural Networks." In Informationssysteme in der Finanzwirtschaft, 385–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-60327-3_28.

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Xiao, Xiao, and Tadashi Dohi. "Estimating Software Reliability Using Extreme Value Distribution." In Communications in Computer and Information Science, 399–406. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27207-3_44.

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Wollover, David. "Accounting for Residual Value and the Probability of War When Estimating Cost-To-Effectiveness Ratios for Evaluating Alternative Military Weapon Systems." In Cost Analysis and Estimating, 143–54. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-3202-5_7.

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WON, YeoJu, SeungWoo KANG, SeongIl UM, and HoChul SHIN. "Problematic of estimating GHG emissions in Logistics Company." In Design for Innovative Value Towards a Sustainable Society, 979–81. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-3010-6_204.

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Castro, Oscar, Angelina Espinoza, and Alfonso Martínez-Martínez. "Estimating the Software Product Value during the Development Process." In Product-Focused Software Process Improvement, 74–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31063-8_7.

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Gu, Qing, and Patricia Lago. "Estimating the Economic Value of Reusable Green ICT Practices." In Safe and Secure Software Reuse, 315–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38977-1_24.

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Smith, Mike, and Ivan T. Robertson. "Making a Decision and Estimating the Value of Selection." In The Theory and Practice of Systematic Staff Selection, 227–44. London: Palgrave Macmillan UK, 1986. http://dx.doi.org/10.1007/978-1-349-07132-6_11.

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Conference papers on the topic "Estimating value"

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Thomas, Paul, and Tom Rowlands. "Estimating the value of automatic disambiguation." In the 30th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1277741.1277875.

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"Estimating Property Value By Replicating One." In 11th European Real Estate Society Conference: ERES Conference 2004. ERES, 2004. http://dx.doi.org/10.15396/eres2004_521.

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Glasserman, Paul, Philip Heidelberger, and Perwez Shahabuddin. "Stratification issues in estimating value-at-risk." In the 31st conference. New York, New York, USA: ACM Press, 1999. http://dx.doi.org/10.1145/324138.324241.

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Fernández, Carlos, Daniel López, Agustín Yagüe, and Juan Garbajosa. "Towards estimating the value of an idea." In the 12th International Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2181101.2181116.

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"Estimating housing market value using regression models." In 8th European Real Estate Society Conference: ERES Conference 2001. ERES, 2001. http://dx.doi.org/10.15396/eres2001_265.

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Kumar, Dilip. "ESTIMATING AND FORECASTING VALUE-AT-RISK USING THE UNBIASED EXTREME VALUE VOLATILITY ESTIMATOR." In 5th Economics & Finance Conference, Miami. International Institute of Social and Economic Sciences, 2016. http://dx.doi.org/10.20472/efc.2016.005.013.

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Sarne, David, and Barbara J. Grosz. "Estimating information value in collaborative multi-agent planning systems." In the 6th international joint conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1329125.1329183.

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Otsuki, Memiko, and Noboru Sonehara. "Estimating the Value of Personal Information with SNS Utility." In 2013 Eighth International Conference on Availability, Reliability and Security (ARES). IEEE, 2013. http://dx.doi.org/10.1109/ares.2013.67.

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"K-NN: ESTIMATING AN ADEQUATE VALUE FOR PARAMETER K." In 10th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001686104590466.

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Carmichael, David G. "Estimating the Value of Built-In Flexibility in Infrastructure." In Construction Research Congress 2018. Reston, VA: American Society of Civil Engineers, 2018. http://dx.doi.org/10.1061/9780784481301.061.

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Reports on the topic "Estimating value"

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Korteweg, Arthur, and Morten Sorensen. Estimating Loan-to-Value and Foreclosure Behavior. Cambridge, MA: National Bureau of Economic Research, March 2012. http://dx.doi.org/10.3386/w17882.

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Gilraine, Michael, Jiaying Gu, and Robert McMillan. A New Method for Estimating Teacher Value-Added. Cambridge, MA: National Bureau of Economic Research, May 2020. http://dx.doi.org/10.3386/w27094.

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Deitchman, Seymour J. Further Exploration in Estimating the Military Value of Training. Fort Belvoir, VA: Defense Technical Information Center, January 1990. http://dx.doi.org/10.21236/ada222059.

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Cabral, Marika, and Mark Cullen. Estimating the Value of Public Insurance Using Complementary Private Insurance. Cambridge, MA: National Bureau of Economic Research, August 2016. http://dx.doi.org/10.3386/w22583.

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Fletcher, Jason, Leora Horwitz, and Elizabeth Bradley. Estimating the Value Added of Attending Physicians on Patient Outcomes. Cambridge, MA: National Bureau of Economic Research, October 2014. http://dx.doi.org/10.3386/w20534.

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Schwartz, R. K., and D. A. Holder. Automated Estimating System (AES), Standard Value Update Program, user`s manual. Office of Scientific and Technical Information (OSTI), August 1994. http://dx.doi.org/10.2172/10182790.

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Gamba-Santamaría, Santiago, Oscar Fernando Jaulín-Méndez, Luis Fernando Melo-Velandia, and Carlos Andrés Quicazán-Moreno. Comparison of methods for estimating the uncertainty of value at risk. Bogotá, Colombia: Banco de la República, February 2016. http://dx.doi.org/10.32468/be.927.

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Belzil, Christian, Arnaud Maurel, and Modibo Sidibé. Estimating the Value of Higher Education Financial Aid: Evidence from a Field Experiment. Cambridge, MA: National Bureau of Economic Research, August 2017. http://dx.doi.org/10.3386/w23641.

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Gagnon, Pieter, Galen L. Barbose, Brady Stoll, Ali Ehlen, Jarret Zuboy, Trieu Mai, and Andrew D. Mills. Estimating the Value of Improved Distributed Photovoltaic Adoption Forecasts for Utility Resource Planning. Office of Scientific and Technical Information (OSTI), May 2018. http://dx.doi.org/10.2172/1437976.

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Gagnon, Pieter, Brady Stoll, Trieu Mai, Ali Ehlen, Galen Barbose, Andrew Mills, and Jarrett Zuboy. Estimating the Value of Improved Distributed Photovoltaic Adoption Forecasts for Utility Resource Planning. Office of Scientific and Technical Information (OSTI), May 2018. http://dx.doi.org/10.2172/1438049.

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