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Journal articles on the topic 'Estimating value'

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Published: 4 June 2021
Last updated: 13 February 2022

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Jung, Hyunhee. "Estimating the social value of multifunctional agriculture (MFA) with choice experiment." Agricultural Economics (Zemědělská ekonomika) 66, No. 3 (March 31, 2020): 120–28. http://dx.doi.org/10.17221/147/2019-agricecon.

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The convergence policy in agriculture, existing in many forms, has globally become one of the main concepts in agricultural development with the "sixth industrial movement" in South Korea and Japan; the multifunctional agriculture (MFA) policies proposed by van der Ploeg and Roep being good examples. The goal of this study is to test three hypotheses through the economic valuation of MFA. In the first part of the research, the importance of agricultural production activity and the core element of the rural complex were evaluated. In the second part, an assessment of three strategies of MFA and its eleven specific attributes was conducted. In the third part, the applicability of the triangular policy of van der Ploeg’s strategic frameworks of MFA was analysed.
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12

Saxena, Shishir, and Andrew McDougall. "Estimating the economic value of libraries." Prometheus 30, no. 3 (September 2012): 367–69. http://dx.doi.org/10.1080/08109028.2012.702056.

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13

Fisman, Raymond. "Estimating the Value of Political Connections." American Economic Review 91, no. 4 (September 1, 2001): 1095–102. http://dx.doi.org/10.1257/aer.91.4.1095.

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14

Knight, Brian. "Estimating the Value of Proposal Power." American Economic Review 95, no. 5 (November 1, 2005): 1639–52. http://dx.doi.org/10.1257/000282805775014290.

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This paper investigates the role of proposal power in the allocation of transportation projects across U.S. congressional districts in 1991 and 1998. The evidence supports the key qualitative prediction of legislative bargaining models: members with proposal power -- those sitting on the transportation authorization committee -- secure more project spending for their districts than do other representatives. Support for the quantitative restrictions on the value of proposal power is more mixed. I then empirically address several alternative models of legislative behavior, including partisan models, informational roles for committees, models with appropriations committees, and theories of committees as preference outliers.
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15

Englin, Jeffrey. "Estimating the amenity value of rainfall." Annals of Regional Science 30, no. 3 (September 1, 1996): 273–83. http://dx.doi.org/10.1007/s001680050026.

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16

Englin, Jeffrey. "Estimating the amenity value of rainfall." Annals of Regional Science 30, no. 3 (September 1996): 273–83. http://dx.doi.org/10.1007/bf01580522.

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17

Burge, Peter, Ann Netten, and Federico Gallo. "Estimating the value of social care." Journal of Health Economics 29, no. 6 (December 2010): 883–94. http://dx.doi.org/10.1016/j.jhealeco.2010.08.006.

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18

Brumm, Thomas J., and Charles R. Hurburgh. "Estimating the processed value of soybeans." Journal of the American Oil Chemists' Society 67, no. 5 (May 1990): 302–7. http://dx.doi.org/10.1007/bf02539680.

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19

Huang, Wei-Qiong, and Wen-Xiang Li. "Physical background on estimating b value." Acta Seismologica Sinica 10, no. 1 (January 1997): 107–14. http://dx.doi.org/10.1007/s11589-997-0045-8.

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20

Einmahl, J. H. J., L. Dehaan, and X. Huang. "Estimating a Multidimensional Extreme-Value Distribution." Journal of Multivariate Analysis 47, no. 1 (October 1993): 35–47. http://dx.doi.org/10.1006/jmva.1993.1069.

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21

Frew, James, and G. Jud. "Estimating the Value of Apartment Buildings." Journal of Real Estate Research 25, no. 1 (January 1, 2003): 77–86. http://dx.doi.org/10.1080/10835547.2003.12091101.

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22

Byström, Hans. "An Alternative Way of Estimating Asset Values and Asset Value Correlations." Journal of Fixed Income 21, no. 2 (September 30, 2011): 30–38. http://dx.doi.org/10.3905/jfi.2011.21.2.030.

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23

Son, Youngdoo, and Wonjoon Kim. "Missing Value Imputation in Stature Estimation by Learning Algorithms Using Anthropometric Data: A Comparative Study." Applied Sciences 10, no. 14 (July 21, 2020): 5020. http://dx.doi.org/10.3390/app10145020.

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Estimating stature is essential in the process of personal identification. Because it is difficult to find human remains intact at crime scenes and disaster sites, for instance, methods are needed for estimating stature based on different body parts. For instance, the upper and lower limbs may vary depending on ancestry and sex, and it is of great importance to design adequate methodology for incorporating these in estimating stature. In addition, it is necessary to use machine learning rather than simple linear regression to improve the accuracy of stature estimation. In this study, the accuracy of statures estimated based on anthropometric data was compared using three imputation methods. In addition, by comparing the accuracy among linear and nonlinear classification methods, the best method was derived for estimating stature based on anthropometric data. For both sexes, multiple imputation was superior when the missing data ratio was low, and mean imputation performed well when the ratio was high. The support vector machine recorded the highest accuracy in all ratios of missing data. The findings of this study showed appropriate imputation methods for estimating stature with missing anthropometric data. In particular, the machine learning algorithms can be effectively used for estimating stature in humans.
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24

Whitehead, John C. "Measuring Use Value from Recreation Participation." Journal of Agricultural and Applied Economics 24, no. 2 (December 1992): 113–19. http://dx.doi.org/10.1017/s0081305200018434.

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AbstractRecreation demand studies have traditionally utilized a two-step valuation method, estimating conditional recreation participation probabilities and then intensity of use decisions. These two steps of analysis are combined to estimate the use value of natural resource recreation sites. The purpose of this paper is to provide a method by which use value can be estimated solely from the participation decision. The one-step resource valuation method allows estimation of use values from coefficients of the logistic regression recreation participation equation. The benefits of the method are the reduced data and effort required to value natural resource areas.
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25

Russell, K. G. "Estimating the Value of e by Simulation." American Statistician 45, no. 1 (February 1991): 66. http://dx.doi.org/10.2307/2685243.

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26

Maevskaya, E. B., and A. L. Gendon. "Information Support of Estimating Industrial Enterprise Value." Vestnik of the Plekhanov Russian University of Economics, no. 4 (August 29, 2019): 118–23. http://dx.doi.org/10.21686/2413-2829-2019-4-118-123.

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27

Wicker, Pamela, Joachim Prinz, and Tassilo von Hanau. "Estimating the value of national sporting success." Sport Management Review 15, no. 2 (May 2012): 200–210. http://dx.doi.org/10.1016/j.smr.2011.08.007.

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28

Lee, Sunghoon, and Mark W. Burris. "Estimating the Option Value of managed lanes." Research in Transportation Economics 70 (October 2018): 28–36. http://dx.doi.org/10.1016/j.retrec.2018.07.016.

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29

Bennett, J. "Estimating the value of Australian environmental assets." Australasian Journal of Environmental Management 18, no. 1 (March 2011): 21–32. http://dx.doi.org/10.1080/14486563.2011.566157.

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30

DOWD, KEVIN. "Estimating Value at Risk: A Subjective Approach." Journal of Risk Finance 1, no. 4 (March 2000): 43–46. http://dx.doi.org/10.1108/eb043454.

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31

Sambucci, Olena, and Julian M. Alston. "Estimating the Value of California Wine Grapes." Journal of Wine Economics 12, no. 2 (May 2017): 149–60. http://dx.doi.org/10.1017/jwe.2017.9.

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AbstractThe California Grape Crush Report (Crush Report) is an authoritative source of information on production and returns per ton by variety of wine grapes that includes summaries of quantities produced and estimates of the average prices and value of wine grapes crushed in California. The data provided in the Crush Report are used to calculate the total value of wine grape production as reported in the annual Agricultural Statistics reports published by the U.S. Department of Agriculture and in major industry publications. We use the differences among crush districts in the shares of production crushed to growers’ accounts to show that the current mechanism of calculating average statewide returns per ton understates the true total value of the crush by 14 to 20 percent. We show that a more accurate estimate of the total value and average price can be obtained if the prices of the wine grapes that are sold are used to infer the prices of wine grapes that are not sold before computing the weighted averages. (JEL Classifications: Q20, Q11, Q13, Q19)
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32

Sykioti, Panagiota, Panagiotis Zis, Athina Vadalouca, Robert van Seventer, and Frank Huygen. "Estimating the Diagnostic Value of DN4 Versions." Pain Practice 14, no. 1 (January 2014): 95–96. http://dx.doi.org/10.1111/papr.12103.

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33

Chau, K. W., S. K. Wong, A. Y. T. Leung, and C. Y. Yiu. "Estimating the value enhancement effects of refurbishment." Facilities 21, no. 1/2 (January 2003): 13–19. http://dx.doi.org/10.1108/02632770310460504.

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34

Mathur, Ike. "Estimating Value of Life per Life Year." Journal of Forensic Economics 3, no. 3 (September 1, 1990): 95–96. http://dx.doi.org/10.5085/0898-5510-3.3.95.

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35

Sumsion, John, Margaret Hawkins, and Anne Morris. "Estimating the economic value of library benefits." Performance Measurement and Metrics 4, no. 1 (April 2003): 13–27. http://dx.doi.org/10.1108/14678040310471211.

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36

PIMM, STUART L. "Estimating the cultural value of the oceans." Nature 387, no. 6630 (May 1997): 231. http://dx.doi.org/10.1038/387231b0.

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37

Knight, William V. "Estimating the value of a geophysical survey." Leading Edge 8, no. 10 (October 1989): 46–48. http://dx.doi.org/10.1190/1.1439577.

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38

Zhou, Xiao-Hua. "Estimating the Mean Value of Occupational Exposures." American Industrial Hygiene Association Journal 59, no. 11 (November 1998): 785–88. http://dx.doi.org/10.1080/15428119891010965.

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39

Hickman, Kent A., Stuart M. Cooper, and Sam Agyei-Ampomah. "Estimating the value of victory: English football." Applied Financial Economics Letters 4, no. 4 (July 25, 2008): 299–302. http://dx.doi.org/10.1080/17446540701720576.

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40

Willmer, M. A. P. "ESTIMATING THE VALUE OF A SMALL FIRM." Management Research News 11, no. 4/5 (April 1988): 17–19. http://dx.doi.org/10.1108/eb027984.

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41

Chang, Wei-Lun, Arleen N. Diaz, and Patrick C. K. Hung. "Estimating trust value: A social network perspective." Information Systems Frontiers 17, no. 6 (July 17, 2014): 1381–400. http://dx.doi.org/10.1007/s10796-014-9519-0.

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42

Lyons, Sean, Edgar Morgenroth, and Richard S. J. Tol. "Estimating the value of lost telecoms connectivity." Electronic Commerce Research and Applications 12, no. 1 (January 2013): 40–51. http://dx.doi.org/10.1016/j.elerap.2012.06.002.

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43

Jang, Wonchang, and Ilsoon Shin. "Estimating Value Creation Effects of i-PIN." Journal of the Korea society of IT services 12, no. 2 (June 30, 2013): 185–93. http://dx.doi.org/10.9716/kits.2013.12.2.185.

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44

Psyck, Elizabeth. "Estimating the Value of a Depository Collection." DttP: Documents to the People 49, no. 1 (April 5, 2021): 9–11. http://dx.doi.org/10.5860/dttp.v49i1.7537.

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In the fall of 2018, I was asked to calculate the value of Grand Valley State University’s (GVSU) general collection (defined as everything except Special Collections and University Archives) as part of risk mitigation planning and updating insurance coverage. Records indicated that our collection’s value was last calculated 11 years earlier, and we lacked both written documentation and institutional memory regarding the process used to calculate that value. While there is a fairly significant body of knowledge around calculating the value of monographs, I struggled to find guidance on calculating the monetary value FDLP collections. There is a robust body of scholarship on promoting the intrinsic value of being a member of the FDLP to library administration and other stakeholders, but very few of them focus on detailed financial benefits of tangible collections.
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45

Kahn, Marc J., and Edward F. Nelling. "Estimating the Value of Medical Education: A Net Present Value Approach." Teaching and Learning in Medicine 22, no. 3 (June 22, 2010): 205–8. http://dx.doi.org/10.1080/10401334.2010.488206.

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46

Bali, Turan G. "An Extreme Value Approach to Estimating Volatility and Value at Risk*." Journal of Business 76, no. 1 (January 2003): 83–108. http://dx.doi.org/10.1086/344669.

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47

Chatain, Olivier, and Denisa Mindruta. "Estimating Value Creation from Revealed Preferences: Application to Value-based Strategies." Strategic Management Journal 38, no. 10 (February 15, 2017): 1964–85. http://dx.doi.org/10.1002/smj.2633.

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48

Tabasi, Hamed, Vahidreza Yousefi, Jolanta Tamošaitienė, and Foroogh Ghasemi. "Estimating Conditional Value at Risk in the Tehran Stock Exchange Based on the Extreme Value Theory Using GARCH Models." Administrative Sciences 9, no. 2 (May 24, 2019): 40. http://dx.doi.org/10.3390/admsci9020040.

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This paper attempted to calculate the market risk in the Tehran Stock Exchange by estimating the Conditional Value at Risk. Since the Conditional Value at Risk is a tail-related measure, Extreme Value Theory has been utilized to estimate the risk more accurately. Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models were used to model the volatility-clustering feature, and to estimate the parameters of the model, the Maximum Likelihood method was applied. The results of the study showed that in the estimation of model parameters, assuming T-student distribution function gave better results than the Normal distribution function. The Monte Carlo simulation method was used for backtesting the Conditional Value at Risk model, and in the end, the performance of different models, in the estimation of this measure, was compared.
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49

Zrazhevska, Nataly. "CONSTRUCTION AND APPLICATION OF THE CLASSIFICATION SCHEME OF DYNAMIC RISK MEASURES ESTIMATING." EUREKA: Physics and Engineering 5 (September 30, 2016): 67–80. http://dx.doi.org/10.21303/2461-4262.2016.00162.

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The most popular methods for dynamic risk measures – Value-at-Risk (VaR) and Conditional VaR (CVaR) estimating were analyzed, description and comparative analysis of the methods were fulfilled, recommendations on the use were given. Results of the research were presented in the form of a classification scheme of dynamic risk measures estimating that facilitates the choice of an estimation method. The GARCH-based models of dynamic risk measures VaR and CVaR evaluation for artificially generated series and two time series of log return on a daily basis of the most well-known Asian stock indexes Nikkey225 Stock Index and CSI30 were constructed to illustrate the effectiveness of the proposed scheme. A qualitative analysis of the proposed models was conducted. To analyze the quality of the dynamic VaR estimations the Cupets test and the Cristoffersen test were used. For CVaR estimations the V-test was used as quality test. The tests results confirm the high quality of obtained estimations. The proposed classification scheme of dynamic risk measures VaR and CVaR estimating may be useful for risk managers of different financial institutions.
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50

Kim, Sung Hee, Yongchan Kwon, KangGeon Kim, and Youngsu Cha. "Estimation of Hand Motion from Piezoelectric Soft Sensor Using Deep Recurrent Network." Applied Sciences 10, no. 6 (March 24, 2020): 2194. http://dx.doi.org/10.3390/app10062194.

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Soft sensors are attracting significant attention in human–machine interaction due to their high flexibility and adaptability. However, estimating motion state from these sensors is difficult due to their nonlinearity and noise. In this paper, we propose a deep learning network for a smart glove system to predict the moving state of a piezoelectric soft sensor. We implemented the network using Long-Short Term Memory (LSTM) units and demonstrated its performance in a real-time system based on two experiments. The sensor’s moving state was estimated and the joint angles were calculated. Since we use moving state in the sensor offset calculation and the offset value is used to estimate the angle value, the accurate moving state estimation results in good performance for angle value estimation. The proposed network performed better than the conventional heuristic method in estimating the moving state. It was also confirmed that the calculated values successfully mimic the joint angles measured using a leap motion controller.
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