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1

Chaudhuri, Surajit, and Vivek Narasayya. "AutoAdmin “what-if” index analysis utility." ACM SIGMOD Record 27, no. 2 (June 1998): 367–78. http://dx.doi.org/10.1145/276305.276337.

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Johansen, JW. "PROVIDER SURVEY OF BISPECTRAL INDEX UTILITY." Anesthesia & Analgesia 86, Supplement (February 1998): 212S. http://dx.doi.org/10.1097/00000539-199802001-00210.

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Clark, Ephraim, Nitin Deshmukh, Celal Barkan Güran, and Konstantino Kassimatis. "Index tracking with utility enhanced weighting." Quantitative Finance 19, no. 11 (June 11, 2019): 1893–904. http://dx.doi.org/10.1080/14697688.2019.1605189.

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4

Savelle, James M., and Max T. Friesen. "An Odontocete (Cetacea) Meat Utility Index." Journal of Archaeological Science 23, no. 5 (September 1996): 713–21. http://dx.doi.org/10.1006/jasc.1996.0067.

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5

Zhang, Jinggong, Ken Seng Tan, and Chengguo Weng. "INDEX INSURANCE DESIGN." ASTIN Bulletin 49, no. 2 (March 27, 2019): 491–523. http://dx.doi.org/10.1017/asb.2019.5.

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AbstractIn this article, we study the problem of optimal index insurance design under an expected utility maximization framework. For general utility functions, we formally prove the existence and uniqueness of optimal contract and develop an effective numerical procedure to derive the optimal solution. For exponential utility and quadratic utility functions, we obtain analytical expression of the optimal indemnity function. Our results show that the indemnity can be a highly nonlinear and even non-monotonic function of the index variable in order to align with the actual loss variable so as to achieve the best reduction in basis risk. Due to the generality of model setup, our proposed method is readily applicable to a variety of insurance applications including index-linked mortality securities, weather index agriculture insurance, and index-based catastrophe insurance. Our method is illustrated by numerical examples where weather index insurance is designed for protection against the adverse rice yield using temperature and precipitation as the underlying indices. Numerical results show that our optimal index insurance significantly outperforms linear-type index insurance contracts in terms of basis risk reduction.
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6

Watkins, Marley W., Joseph C. Kush, and Barbara A. Schaefer. "Diagnostic Utility of the Learning Disability Index." Journal of Learning Disabilities 35, no. 2 (March 2002): 98–103. http://dx.doi.org/10.1177/002221940203500201.

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7

Kaambwa, Billingsley, Lucinda Billingham, and Stirling Bryan. "Mapping utility scores from the Barthel index." European Journal of Health Economics 14, no. 2 (November 2, 2011): 231–41. http://dx.doi.org/10.1007/s10198-011-0364-5.

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8

Biagtan, Mark J., Ravi K. Viswanathan, Michael D. Evans, and Sameer K. Mathur. "Clinical utility of the Chronic Urticaria Index." Journal of Allergy and Clinical Immunology 127, no. 6 (June 2011): 1626–27. http://dx.doi.org/10.1016/j.jaci.2011.01.045.

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9

Geevasinga, Nimeshan, James Howells, Parvathi Menon, Mehdi van den Bos, Kazumoto Shibuya, José Manuel Matamala, Susanna B. Park, Karen Byth, Matthew C. Kiernan, and Steve Vucic. "Amyotrophic lateral sclerosis diagnostic index." Neurology 92, no. 6 (January 11, 2019): e536-e547. http://dx.doi.org/10.1212/wnl.0000000000006876.

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ObjectiveThe aim of the study was to assess the utility of a novel amyotrophic lateral sclerosis (ALS) diagnostic index (ALSDI).MethodsA prospective multicenter study was undertaken on patients presenting with suspected ALS. The reference standard (Awaji criteria) was applied to all patients at recruitment. Patients were randomly assigned to a training (75%) and a test (25%) cohort. The ALSDI was developed in the training cohort and its diagnostic utility was subsequently assessed in the test cohort.ResultsA total of 407 patients were recruited, with 305 patients subsequently diagnosed with ALS and 102 with a non-ALS mimicking disorder. The ALSDI reliably differentiated ALS from neuromuscular disorders in the training cohort (area under the curve 0.92, 95% confidence interval 0.89–0.95), with ALSDI ≥4 exhibiting 81.6% sensitivity, 89.6% specificity, and 83.5% diagnostic accuracy. The ALSDI diagnostic utility was confirmed in the test cohort (area under the curve 0.90, 95% confidence interval 0.84–0.97), with ALSDI ≥4 exhibiting 83.3% sensitivity, 84% specificity, and 83.5% diagnostic accuracy. In addition, the diagnostic utility of the ALSDI was confirmed in patients who were Awaji negative at recruitment and in those exhibiting a predominantly lower motor neuron phenotype.ConclusionThe ALSDI reliably differentiates ALS from mimicking disorders at an early stage in the disease process.Classification of evidenceThis study provides Class I evidence that for patients with suspected ALS, the ALSDI distinguished ALS from neuromuscular mimicking disorders.
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10

Goyal, A., N. Mittal, P. Mittal, and K. Gauba. "Bispectral Index Monitoring:Validity and Utility in Pediatric Dentistry." Journal of Clinical Pediatric Dentistry 38, no. 4 (July 1, 2014): 366–69. http://dx.doi.org/10.17796/jcpd.38.4.17j63w8013614933.

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Reliable and safe provision of sedation and general anesthesia is dependent on continuous vigilance of patient’s sedation depth. Failure to do so may result in unintended oversedation or undersedation. It is a common practice to observe sedation depth by applying subjective sedation scales and in case of general anesthesia, practitioner is dependent on vital sign assessment. The Bispectral Index System (BIS) is a recently introduced objective, quantitative, easy to use, and free from observer bias, and clinically useful tool to assess sedation depth and it precludes the need to stimulate the patient to assess his sedation level. The present article is an attempt to orient the readers towards utility and validity of BIS for sedation and general anesthesia in pediatric dentistry. In this article, we attempt to make the readers understand the principle of BIS, its variation across sedation continuum, its validity across different age groups and for a variety of sedative drugs.
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11

Bromley, B., T. Shipp, and B. R. Benacerraf. "Genetic sonogram scoring index: accuracy and clinical utility." Journal of Ultrasound in Medicine 18, no. 8 (August 1999): 523–28. http://dx.doi.org/10.7863/jum.1999.18.8.523.

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12

Stevens, Katherine. "Valuation of the Child Health Utility 9D Index." PharmacoEconomics 30, no. 8 (August 2012): 729–47. http://dx.doi.org/10.2165/11599120-000000000-00000.

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13

Rodriguez Violante, M., A. Cervantes Arriaga, H. Soto Molina, J. P. Díaz Martínez, and M. Pizarro Castellanos. "Vas Eq-5d Utility Index In Parkinson’s Disease." Value in Health 17, no. 3 (May 2014): A63. http://dx.doi.org/10.1016/j.jval.2014.03.369.

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14

Roussel, Nancye C., and Mary Lobdell. "The clinical utility of the soft phonation index." Clinical Linguistics & Phonetics 20, no. 2-3 (January 2006): 181–86. http://dx.doi.org/10.1080/02699200400026942.

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15

Schold, J. D., and B. Kaplan. "Living Kidney Donor Profile Index: Utility and Limitations." American Journal of Transplantation 16, no. 7 (March 3, 2016): 1951–52. http://dx.doi.org/10.1111/ajt.13727.

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Bonafoni, Stefania. "Spectral index utility for summer urban heating analysis." Journal of Applied Remote Sensing 9, no. 1 (July 27, 2015): 096030. http://dx.doi.org/10.1117/1.jrs.9.096030.

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17

Cooper, H. S., and R. H. Epstein. "A437 CLINICAL UTILITY OF THE BISPECTRAL INDEX (BIS)." Anesthesiology 87, Supplement (September 1997): 437A. http://dx.doi.org/10.1097/00000542-199709001-00437.

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18

Crans, Gerald G., Harry K. Genant, and John H. Krege. "Prognostic utility of a semiquantitative spinal deformity index." Bone 37, no. 2 (August 2005): 175–79. http://dx.doi.org/10.1016/j.bone.2005.04.003.

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19

Franchin, Marilyn, Daniel Jost, Hugues Lefort, Stephane Travers, and Jean-Pierre Tourtier. "Utility of shock index calculation in hemorrhagic trauma." American Journal of Emergency Medicine 33, no. 7 (July 2015): 978. http://dx.doi.org/10.1016/j.ajem.2015.04.001.

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20

Sindi, Hatem, and Ehab El-Saadany. "Unified Reliability Index Development for Utility Performance Assessment." Intelligent Industrial Systems 2, no. 2 (June 2016): 149–61. http://dx.doi.org/10.1007/s40903-016-0047-8.

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21

Savelle, James M., Max T. Friesen, and Lee R. Lyman. "Derivation and Application of an Otariid Utility Index." Journal of Archaeological Science 23, no. 5 (September 1996): 705–12. http://dx.doi.org/10.1006/jasc.1996.0066.

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22

Casquilho, José Pinto. "Combining Expected Utility and Weighted Gini-Simpson Index into a Non-Expected Utility Device." Theoretical Economics Letters 05, no. 02 (2015): 185–95. http://dx.doi.org/10.4236/tel.2015.52023.

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23

Satpute, Prof Pratik R. "Concept of Room up Sell Index and its Utility in Rooms Division in Star Rated Hotels." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 423–25. http://dx.doi.org/10.31142/ijtsrd22805.

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24

Metcalfe, Duncan, and Kevin T. Jones. "A Reconsideration of Animal Body-Part Utility Indices." American Antiquity 53, no. 3 (July 1988): 486–504. http://dx.doi.org/10.2307/281213.

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The animal body-part utility indices developed by Lewis Binford have been used to interpret faunal assemblages ranging from Plio-Pleistocene sites in East Africa to a late prehistoric bison kill in the High Plains of North America. Little attention, however, has been placed on refining or further developing these scales of economic utility. We examine Binford's derivation of the modified general utility index (MGUI) and demonstrate that it is needlessly complex. A nearly identical index, the food utility index (FUI), is presented. It simply scales variation in the amount of meat, marrow, and bone grease associated with different caribou body parts. We then use the insights provided by this simple scale to explore relations among economic utility, differential body-part representation, and human decision making.
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25

Ahluwalia, Ranbir, Jarrett Foster, Madeleine M. Sherburn, Georgina E. Sellyn, Katherine A. Kelly, Muhammad Owais Abdul Ghani, Alyssa L. Wiseman, Chevis N. Shannon, and Christopher M. Bonfield. "Deformational brachycephaly: the clinical utility of the cranial index." Journal of Neurosurgery: Pediatrics 26, no. 2 (August 2020): 122–26. http://dx.doi.org/10.3171/2020.2.peds19767.

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OBJECTIVEThe incidence of deformational brachycephaly has risen since the “Back to Sleep” movement in 1992 by the American Academy of Pediatrics. Brachycephaly prevalence and understanding the dynamic nature of the pediatric skull have not been explored in relation to the cranial index (CI). The objective of the study was to determine the prevalence of brachycephaly, via the CI, with respect to time.METHODSThe authors conducted a retrospective review of 1499 patients ≤ 19 years of age who presented for trauma evaluation with a negative CT scan for trauma (absence of bleed) in 2018. The CI was calculated using CT at the lateral-most point of the parietal bone (cephalic width), and the distance from the glabella to the opisthocranion (cephalic length). Brachycephaly was defined as a CI ≥ 90%.RESULTSThe mean CI was 82.6, with an average patient age of 6.8 years. The prevalence of deformational brachycephaly steadily decreased from 27% to 4% from birth to > 2 years of life. The mean CI was statistically different between ages < 12 months, 12–24 months, and > 24 months (F[2,1496] = 124.058, p < 0.0005). A simple linear regression was calculated to predict the CI based on age; the CI was found to decrease by 0.038 each month. A significant regression equation was found (F[1,1497] = 296.846, p < 0.0005), with an R2 of 0.140.CONCLUSIONSThe incidence of deformational brachycephaly is common in infants but decreases as the child progresses through early childhood. Clinicians can expect a significant decrease in mean CI at 12 and 24 months. Additionally, these regression models show that clinicians can expect continued improvement throughout childhood.
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26

Ghushchyan, V. H., P. W. Sullivan, and A. M. Libby. "Applied Contributions To The Eq-5D Health Utility Index." Value in Health 16, no. 3 (May 2013): A8. http://dx.doi.org/10.1016/j.jval.2013.03.046.

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27

Pieri, M., M. Storto, S. Pignalosa, R. Zenobi, F. Buttari, S. Bernardini, D. Centonze, and M. Dessi. "KFLC Index utility in multiple sclerosis diagnosis: Further confirmation." Journal of Neuroimmunology 309 (August 2017): 31–33. http://dx.doi.org/10.1016/j.jneuroim.2017.05.007.

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28

Lee, Jung Hwa. "Utility of the Glycemic Index in Practical Diabetes Management." Journal of Korean Diabetes 16, no. 2 (2015): 135. http://dx.doi.org/10.4093/jkd.2015.16.2.135.

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29

Fehr, M. "The exergy carry-over index for a utility system." Heat Recovery Systems and CHP 15, no. 8 (November 1995): 797–801. http://dx.doi.org/10.1016/0890-4332(94)00086-z.

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30

Ouellet, Daniele. "Benefit–risk assessment: the use of clinical utility index." Expert Opinion on Drug Safety 9, no. 2 (February 23, 2010): 289–300. http://dx.doi.org/10.1517/14740330903499265.

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31

Tseng, Jim, and Kenneth Nugent. "Utility of the Shock Index in Patients With Sepsis." American Journal of the Medical Sciences 349, no. 6 (June 2015): 531–35. http://dx.doi.org/10.1097/maj.0000000000000444.

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32

Ramesh, G. H., T. Sudeep, and T. V. Ramakrishnan. "Utility of Shock Index to Predict Ruptured Ectopic Pregnancy." Academic Emergency Medicine 14, no. 5 (March 26, 2007): e111-e112. http://dx.doi.org/10.1197/j.aem.2007.02.011.

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33

Gumasta, Kapil, Santosh Kumar Gupta, Lyes Benyoucef, and M. K. Tiwari. "Developing a reconfigurability index using multi-attribute utility theory." International Journal of Production Research 49, no. 6 (March 15, 2011): 1669–83. http://dx.doi.org/10.1080/00207540903555536.

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34

Liu, Sifeng, Yaoguo Dang, and Yi Lin. "Synthetic utility index method and venturous capital decision‐making." Kybernetes 33, no. 2 (February 2004): 288–94. http://dx.doi.org/10.1108/03684920410514238.

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35

Meads, D. M., S. P. McKenna, N. Doughty, C. Das, W. Gin-Sing, J. Langley, and J. Pepke-Zaba. "The responsiveness and validity of the CAMPHOR Utility Index." European Respiratory Journal 32, no. 6 (December 1, 2008): 1513–19. http://dx.doi.org/10.1183/09031936.00069708.

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36

Åsberg, Arne, Gustav Mikkelsen, and Ingrid Hov Odsæter. "A new index of clinical utility for diagnostic tests." Scandinavian Journal of Clinical and Laboratory Investigation 79, no. 8 (November 1, 2019): 560–65. http://dx.doi.org/10.1080/00365513.2019.1677938.

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37

McCabe, Christopher J., Katherine J. Stevens, and John E. Brazier. "Utility Scores for the Health Utilities Index Mark 2." Medical Care 43, no. 6 (June 2005): 627–35. http://dx.doi.org/10.1097/01.mlr.0000163666.00471.8e.

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38

Kyskan, Robert, Warren Davidson, Don Sin, and Tony R. Bai. "Utility of the Lipid Macrophage Index in Induced Sputum." Chest 138, no. 4 (October 2010): 154A. http://dx.doi.org/10.1378/chest.9482.

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39

Didier, Thomas, Clarence D. Kreiter, Russell Buri, and Catherine Solow. "Investigating the Utility of a GPA Institutional Adjustment Index." Advances in Health Sciences Education 11, no. 2 (May 2006): 145–53. http://dx.doi.org/10.1007/s10459-005-0390-0.

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40

Berg, Sanford V. "The customer bill as an index of utility performance." Electricity Journal 8, no. 1 (January 1995): 54–59. http://dx.doi.org/10.1016/1040-6190(95)90175-2.

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41

Hodoshima, Jiro, and Yoshio Miyahara. "Utility indifference pricing and the Aumann–Serrano performance index." Journal of Mathematical Economics 86 (January 2020): 83–89. http://dx.doi.org/10.1016/j.jmateco.2019.12.002.

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42

Harris, William S. "The Omega-3 Index: Clinical Utility for Therapeutic Intervention." Current Cardiology Reports 12, no. 6 (September 1, 2010): 503–8. http://dx.doi.org/10.1007/s11886-010-0141-6.

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43

Koch, Erica, Shannon Lovett, Trac Nghiem, Robert Riggs, and Megan A. Rech. "Shock index in the emergency department: utility and limitations." Open Access Emergency Medicine Volume 11 (August 2019): 179–99. http://dx.doi.org/10.2147/oaem.s178358.

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Wu, Hongliang, Daoxin Peng, and Ling Wang. "Research on Utility Evaluation of Grid Investment considering Risk Preference of Decision-Makers." Mathematical Problems in Engineering 2020 (July 29, 2020): 1–16. http://dx.doi.org/10.1155/2020/3568470.

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Effectiveness evaluations are one of the important ways to guide grid investment and to improve investment efficiency. Improving the effectiveness of grid investment evaluations is studied based on the optimization of the investment evaluation index system and the utility evaluation model. The index system is optimized by establishing an evaluation index system of grid investment effectiveness, considering the redundancy between the indices, and constructing an ISM-DEA model. The utility function model was introduced to fully consider the different risk appetites of decision-makers, and a utility evaluation model that takes risk appetite into account was established. An improved weight integration model based on multiobjective optimization was established by considering the minimum deviation and the trend-optimal objective function when setting the index weights. The calculation results show that the feasibility of the index system optimization model and utility evaluation model constructed in this study is verified under the premise of satisfying the assumptions. By adjusting the risk preference coefficient of decision-makers, the dynamic optimization of the grid investment utility evaluation results can be realized.
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45

Panchangam, Chaitanya, Eric Dean Merrill, and Geetha Raghuveer. "Utility of arterial stiffness assessment in children." Cardiology in the Young 28, no. 3 (January 9, 2018): 362–76. http://dx.doi.org/10.1017/s1047951117002402.

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AbstractAtherosclerotic cardiovascular disease is a leading cause of death and disability worldwide, and the atherosclerotic process begins in childhood. Prevention or containment of risk factors that accelerate atherosclerosis can delay the development of atherosclerotic cardiovascular disease. Although current recommendations are to periodically screen for commonly prevailing risk factors for atherosclerosis in children, a single test that could quantify the cumulative effect of all risk factors on the vasculature, thus assessing arterial health, would be helpful in further stratifying risk. Measurement of pulse wave velocity and assessment of augmentation index – measures of arterial stiffness – are easy-to-use, non-invasive methods of examining arterial health. Various studies have assessed pulse wave velocity and augmentation index in children with commonly occurring conditions including obesity, hypertension, insulin resistance, diabetes mellitus, dyslipidaemia, physical inactivity, chronic kidney disease, CHD and acquired heart diseases, and in children who were born premature or small for gestational age. This article summarises pulse wave velocity and augmentation index assessments and the effects of commonly prevailing chronic conditions on arterial health in children. In addition, currently available reference values for pulse wave velocity and augmentation index in healthy children are included. Further research to establish widely applicable normative values and the effect of lifestyle and pharmacological interventions on arterial health in children is needed.
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46

McIntosh, Craig, Felix Povel, and Elisabeth Sadoulet. "Utility, Risk and Demand for Incomplete Insurance: Lab Experiments with Guatemalan Co-Operatives." Economic Journal 129, no. 622 (April 3, 2019): 2581–607. http://dx.doi.org/10.1093/ej/uez005.

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Abstract We play a series of incentivised laboratory games with risk-exposed co-operativised Guatemalan coffee farmers to understand the demand for index-based rainfall insurance. We estimate an explicit utility curve for every player and hence predict expected utility demand under counterfactual scenarios. Using these estimates, we provide a precise money-metric decomposition of the extent to which the low observed demand for index insurance is driven by expected utility theory, or by behavioural issues arising from a prospect-style utility structure. Our results suggest that consumers value probabilistic insurance using a prospect-style utility function that is concave both in probabilities and in income.
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47

YOSHIDA, YUJI. "WEIGHTED QUASI-ARITHMETIC MEANS AND A RISK INDEX FOR STOCHASTIC ENVIRONMENTS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 19, supp01 (December 2011): 1–16. http://dx.doi.org/10.1142/s0218488511007313.

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In this paper, the weighted quasi-arithmetic means are discussed from the viewpoint of utility functions and downward risks in economics. Representing the weighting functions by probability density functions and the conditional expectations, an index for downward risks in stochastic environments is derived. This paper discusses the relation among the index, the first-order stochastic dominance and the risk premium in economics, and further it investigates the relation between the index and value-at-risks which are known as another estimation for downward risks in finance. Finally, this paper shows a lot of examples of the weighted quasi-arithmetic mean and the aggregated mean ratio for various typical utility functions with various typical utility functions and probability density functions.
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48

Yao, Qiang, Chaojie Liu, Yaoguang Zhang, and Ling Xu. "Health-Related Quality of Life of People with Self-Reported Hypertension: A National Cross-Sectional Survey in China." International Journal of Environmental Research and Public Health 16, no. 10 (May 16, 2019): 1721. http://dx.doi.org/10.3390/ijerph16101721.

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This study aimed to determine the health-related quality of life (HRQoL) of people with self-reported diagnosed hypertension and its determinants in China. Data was obtained from the 5th National Health Services Survey. The HRQoL of the respondents who were 15 years or older was assessed with the EQ-5D-3L utility index and visual analogue scale (VAS), and compared between those with (n = 30,063) and without (n = 158,657) self-reported hypertension. Multivariate logistic regression, Tobit regression, and linear regression models were established to identify predictors of HRQoL. A difference of half standard deviation was deemed as minimal clinically important difference (MCID) for the utility index (0.03). The respondents with self-reported hypertension were more likely to report problems in the five dimensions (Adjusted Odds Ratio = 1.43–1.70) of the EQ-5D-3L, resulting in a significant lower utility index (β = −0.04) and VAS scores (β = −3.22) compared with those without self-reported hypertension, and the difference of the utility index exceeded MCID. In the respondents with self-reported hypertension, higher utility index and VAS scores were found in those who were female, younger, married, employed, smoking, drinking, exercising regularly, absent from comorbidity, resided in the eastern developed region, had normal body mass index, higher levels of education, and income. Hypertension management programs were associated with higher utility index (β = 0.01) and VAS scores (β = 1.02). Overall, hypertension is associated with lower HRQoL. Higher socioeconomic status and participation in management programs for chronic conditions are independent predictors of higher HRQoL of hypertensive people. This study provides a national representative estimate on the HRQoL of hypertensive people in China, which can be used for calculating the burden of hypertension.
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49

AYU DEVI, KADEK FRISCA, KOMANG DHARMAWAN, and NI MADE ASIH. "ANALISIS PORTOFOLIO SAHAM LQ45 MENGGUNAKAN FUNGSI UTILITAS KUADRATIK." E-Jurnal Matematika 2, no. 1 (January 30, 2013): 33. http://dx.doi.org/10.24843/mtk.2013.v02.i01.p025.

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Utility function can use to give risk preference for investors who want to get the benefits gained meets investment targets. Quadratic utility functions on optimal portfolio is strongly influenced by the expected return and standard deviation. The establishment of optimal portfolios using a quadratic utility function optimization problems. Under the settlement portfolio optimization, the necessary data is expected return, variance, and variance covariance matrix. The optimal portfolio is affected by some factors Risky less Rate, risk aversion index, and Borrow Rate. The results of settlement portfolio optimization is obtaining the utility value while the relatively large changes influencing by risk averse index.
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50

Revicki, Dennis A., Mary Kay Margolis, Christine L. Thompson, Eli O. Meltzer, David W. Sandor, and James W. Shaw. "Major Symptom Score Utility Index for Patients with Acute Rhinosinusitis." American Journal of Rhinology & Allergy 25, no. 3 (May 2011): e99-e106. http://dx.doi.org/10.2500/ajra.2011.25.3575.

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