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Journal articles on the topic 'Under average'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Liao, Ming, and Longmin Wang. "Average under the Iwasawa transformation." Proceedings of the American Mathematical Society 135, no. 3 (2006): 895–901. http://dx.doi.org/10.1090/s0002-9939-06-08508-x.

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2

Espinoza-Audelo, Luis F., Ernesto León-Castro, Marycruz Olazabal-Lugo, José M. Merigó, and Anna M. Gil-Lafuente. "Using Ordered Weighted Average for Weighted Averages Inflation." International Journal of Information Technology & Decision Making 19, no. 02 (2020): 601–28. http://dx.doi.org/10.1142/s0219622020500066.

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This paper presents the ordered weighted average weighted average inflation (OWAWAI) and some extensions using induced and heavy aggregation operators and presents the generalized operators and some of their families. The main advantage of these new formulations is that they can use two different sets of weighting vectors and generate new scenarios based on the reordering of the arguments with the weights. With this idea, it is possible to generate new approaches that under- or overestimate the results according to the knowledge and expertise of the decision-maker. The work presents an applica

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3

Carbone, P., and D. Petri. "Average power estimation under nonsinusoidal conditions." IEEE Transactions on Instrumentation and Measurement 49, no. 2 (2000): 333–36. http://dx.doi.org/10.1109/19.843073.

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4

Liu, Zheng, Jianjun Miao, and Dongling Su. "Fiscal Stimulus Under Average Inflation Targeting." Federal Reserve Bank of San Francisco, Working Paper Series 2022, no. 22 (2022): 01–48. http://dx.doi.org/10.24148/wp2022-22.

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The stimulus effects of expansionary fiscal policy under average inflation targeting (AIT) depends on both monetary and fiscal policy regimes. AIT features an inflation makeup under the monetary regime, but not under the fiscal regime. In normal times, AIT amplifies the short-run fiscal multipliers under both regimes while mitigating the cumulative multiplies due to intertemporal substitution. In a zero-lower-bound (ZLB) period, AIT reduces fiscal multipliers under a monetary regime by shortening the duration of the ZLB through expected inflation makeup. Under the fiscal regime, AIT has a nonl

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5

Wang, Yan, and Hehui Wu. "Graph partitions under average degree constraint." Journal of Combinatorial Theory, Series B 165 (March 2024): 197–210. http://dx.doi.org/10.1016/j.jctb.2023.11.006.

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6

Blank, Michael. "Average shadowing revisited." Nonlinearity 37, no. 6 (2024): 065006. http://dx.doi.org/10.1088/1361-6544/ad3ac6.

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Abstract We propose a novel unifying approach to study the shadowing property for a broad class of dynamical systems (in particular, discontinuous and non-invertible) under a variety of perturbations. In distinction to known constructions, our approach is local: it is based on the gluing property which takes into account the shadowing under a single (not necessarily small) perturbation.

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7

Rand, Alexander. "Average Interpolation under the Maximum Angle Condition." SIAM Journal on Numerical Analysis 50, no. 5 (2012): 2538–59. http://dx.doi.org/10.1137/10081842x.

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8

Hosaka, Masanori, Masayuki Horiguchi, and Masami Kurano. "Controlled Markov set-chains under average criteria." Applied Mathematics and Computation 120, no. 1-3 (2001): 195–209. http://dx.doi.org/10.1016/s0096-3003(99)00241-6.

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9

Bera, Suman K., Syamantak Das, and Amit Kumar. "Minimizing average flow-time under knapsack constraint." Theoretical Computer Science 609 (January 2016): 516–25. http://dx.doi.org/10.1016/j.tcs.2015.04.031.

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10

Aslam, Muhammad, Khushnoor Khan, Mohammed Albassam, and Liaquat Ahmad. "Moving average control chart under neutrosophic statistics." AIMS Mathematics 8, no. 3 (2023): 7083–96. http://dx.doi.org/10.3934/math.2023357.

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<abstract> <p>Continuous monitoring and improving the production process is a crucial step for the entrepreneur to maintain its position in the market. A successful process monitoring scheme depends upon the specification of the quality being monitored. In this paper, the monitoring of temperature is addressed using the specification of moving average under uncertainty. We determined the coefficients of the proposed chart utilizing the Monte Carlo simulation for a different measure of indeterminacy. The efficiency of the proposed chart has been evaluated by determining the average

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11

CONTRERAS, GONZALO, and RENATO ITURRIAGA. "Average linking numbers." Ergodic Theory and Dynamical Systems 19, no. 6 (1999): 1425–35. http://dx.doi.org/10.1017/s0143385799146777.

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We prove the existence of the average linking number, also called the Hopf invariant, for any invariant measure under a differentiable flow on $S^3$ without singularities, which has no periodic orbit of positive measure.

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12

Danek, Tomasz, and Michael A. Slawinski. "Backus Average Under Random Perturbations of Layered Media." SIAM Journal on Applied Mathematics 76, no. 4 (2016): 1239–49. http://dx.doi.org/10.1137/15m1043170.

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13

Tahani, Nabil. "Exotic Geometric Average Options Pricing under Stochastic Volatility." Applied Mathematical Finance 20, no. 3 (2013): 229–45. http://dx.doi.org/10.1080/1350486x.2012.678735.

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14

Holcomb, Jennifer, Jeanelle Sheeder, Stephen Scott, and Catherine Stevens-Simon. "When Are Pregnant Teenagers Under, Average, or Overweight?" Journal of Pediatric and Adolescent Gynecology 20, no. 2 (2007): S120. http://dx.doi.org/10.1016/j.jpag.2007.03.025.

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15

Yamazaki, Akira. "Pricing average options under time-changed Lévy processes." Review of Derivatives Research 17, no. 1 (2013): 79–111. http://dx.doi.org/10.1007/s11147-013-9091-7.

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16

Xu, R. "Estimating average regression effect under non-proportional hazards." Biostatistics 1, no. 4 (2000): 423–39. http://dx.doi.org/10.1093/biostatistics/1.4.423.

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17

Yamaguchi, Rintaro. "Wealth and population growth under dynamic average utilitarianism." Environment and Development Economics 23, no. 1 (2017): 1–18. http://dx.doi.org/10.1017/s1355770x17000274.

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AbstractIn wealth accounting and sustainability assessments, we characterize the non-declining wealth criterion under dynamic average utilitarianism (DAU) as defined by Dasgupta (2001). Under DAU, the objective function consists of total intergenerational well-being divided by the present discounted sum of population from the present to the future. It is shown that, in order for an economy to be on a sustainable path, inclusive wealth should grow at a rate higher than the difference between the discount rate and the share of current population of the discounted future population. Our applicati

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18

Jonker, M. A., and A. W. van der Vaart. "Estimation of average mortality under censoring and truncation." Journal of Population Research 22, no. 1 (2005): 49–62. http://dx.doi.org/10.1007/bf03031803.

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19

KELMANS, G. K. "Identification of ‘ moving average’ plants under unobservable disturbances." International Journal of Systems Science 16, no. 3 (1985): 301–12. http://dx.doi.org/10.1080/00207728508926674.

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20

Boadway, Robin, and Laurence Jacquet. "Optimal marginal and average income taxation under maximin." Journal of Economic Theory 143, no. 1 (2008): 425–41. http://dx.doi.org/10.1016/j.jet.2008.01.003.

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21

Currier, Kevin M. "Strategic Firm Behavior Under Average-Revenue-Lagged Regulation." Journal of Regulatory Economics 27, no. 1 (2005): 67–79. http://dx.doi.org/10.1007/s11149-004-4419-8.

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22

Mochida, Tohru, and Tetsumi Horikoshi. "Characteristics of wettedness under constant average skin temperature." Journal of Thermal Biology 18, no. 5-6 (1993): 449–54. http://dx.doi.org/10.1016/0306-4565(93)90075-5.

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23

Templeton, Alan R. "The general relationship between average effect and average excess." Genetical Research 49, no. 1 (1987): 69–70. http://dx.doi.org/10.1017/s0016672300026756.

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SummaryThe average effect and average excess both measure the phenotypic effects of gametes in a population. A matrix notation is introduced that provides a general analytical solution for the average effects at a single locus with k alleles that can be solved for any population regardless of its genotype frequencies. This same notation also provides an easy way of deriving and generalizing to k alleles the well-known relationships between average effects and average excesses that exist under random-mating and regular deviations from Hardy–Weinberg equilibrium due to inbreeding.

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24

Wang, Yue, Alvaro Velasquez, George Atia, Ashley Prater-Bennette, and Shaofeng Zou. "Robust Average-Reward Reinforcement Learning." Journal of Artificial Intelligence Research 80 (June 16, 2024): 719–803. http://dx.doi.org/10.1613/jair.1.15451.

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Robust Markov decision processes (MDPs) aim to find a policy that optimizes the worst-case performance over an uncertainty set of MDPs. Existing studies mostly have focused on the robust MDPs under the discounted reward criterion, leaving the ones under the average-reward criterion largely unexplored. In this paper, we develop the first comprehensive and systematic study of robust average-reward MDPs, where the goal is to optimize the long-term average performance under the worst case. Our contributions are four-folds: (1) we prove the uniform convergence of the robust discounted value functio

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25

LIU, YEHONG, and GUOSHENG YIN. "AVERAGE HOLDING PRICE." Annals of Financial Economics 13, no. 01 (2018): 1850002. http://dx.doi.org/10.1142/s2010495218500021.

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We introduce a new concept of the average holding price (AHP) in the stock market. We show that, under certain assumptions on the investors’ behaviors, the AHP of a stock can be estimated using the historical trading prices and volumes. In contrast to the moving average of the stock price or the volume weighted average price, the AHP serves as a more objective benchmark for estimating the average profit or loss level of the stockholders. The algorithms for estimating the AHP are developed. Simulation studies show that the true AHP can be estimated accurately using our algorithm.

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26

Li, Jie. "PRECISE ASYMPTOTICS OF MOVING AVERAGE PROCESS UNDER ?-MIXING ASSUMPTION." Journal of the Korean Mathematical Society 49, no. 2 (2012): 235–49. http://dx.doi.org/10.4134/jkms.2012.49.2.235.

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27

MingRen, Yu. "Level Reset Option Pricing Model Under the Geometric Average." International Journal of u- and e- Service, Science and Technology 9, no. 10 (2016): 93–102. http://dx.doi.org/10.14257/ijunesst.2016.9.10.09.

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28

Basak, Gopal K., Arinab bisi, and Mrinal K. Ghosh. "Controlled random degenerate diffusions under long-run average cost." Stochastics and Stochastic Reports 61, no. 1-2 (1997): 121–40. http://dx.doi.org/10.1080/17442509708834119.

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29

Shyichuk, A. V. "Viscosity-average molecular weight changes under crosslinking of macromolecules." European Polymer Journal 33, no. 4 (1997): 437–39. http://dx.doi.org/10.1016/s0014-3057(96)00218-2.

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30

Chen, Pingyan, Tien-Chung Hu, and Andrei Volodin. "Limiting behaviour of moving average processes under -mixing assumption." Statistics & Probability Letters 79, no. 1 (2009): 105–11. http://dx.doi.org/10.1016/j.spl.2008.07.026.

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31

Rose, Christopher, and Roy Yates. "Minimizing the average cost of paging under delay constraints." Wireless Networks 1, no. 2 (1995): 211–19. http://dx.doi.org/10.1007/bf01202543.

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32

Bhattacharya, Rabi N., and Mukul Majumdar. "Controlled semi-Markov models under long-run average rewards." Journal of Statistical Planning and Inference 22, no. 2 (1989): 223–42. http://dx.doi.org/10.1016/0378-3758(89)90113-4.

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33

Zhengyan, Lin, and Li Degui. "Strong Approximation for Moving Average Processes Under Dependence Assumptions." Acta Mathematica Scientia 28, no. 1 (2008): 217–24. http://dx.doi.org/10.1016/s0252-9602(08)60023-5.

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34

Davidson, R. Andrew, and Ting Guo. "Average Physical Enhancement by Nanomaterials under X-ray Irradiation." Journal of Physical Chemistry C 118, no. 51 (2014): 30221–28. http://dx.doi.org/10.1021/jp509471m.

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35

Zhang, Li-Xin. "Complete convergence of moving average processes under dependence assumptions." Statistics & Probability Letters 30, no. 2 (1996): 165–70. http://dx.doi.org/10.1016/0167-7152(95)00215-4.

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36

Munari, Cosimo, Justin Plückebaum, and Stefan Weber. "Robust Portfolio Selection under Recovery Average Value at Risk." SIAM Journal on Financial Mathematics 15, no. 1 (2024): 295–314. http://dx.doi.org/10.1137/23m1555491.

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37

MOON, MARY ANN. "Flexible Sigmoidoscopy Best for Average-Risk People Under 60." Ob.Gyn. News 42, no. 11 (2007): 39. https://doi.org/10.1016/s0029-7437(07)70514-1.

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38

S TAILOR, KALPESH. "Exponential Weighted Moving Average (EWMA) Chart Under The Assumption of Moderateness And Its 3∆ Control Limits." Mathematical Journal of Interdisciplinary Sciences 5, no. 2 (2017): 121–28. http://dx.doi.org/10.15415/mjis.2017.52009.

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39

Bagaram, Martin B., and Sándor F. Tóth. "Multistage Sample Average Approximation for Harvest Scheduling under Climate Uncertainty." Forests 11, no. 11 (2020): 1230. http://dx.doi.org/10.3390/f11111230.

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Forest planners have traditionally used expected growth and yield coefficients to predict future merchantable timber volumes. However, because climate change affects forest growth, the typical forest planning methods using expected value of forest growth can lead to sub-optimal harvest decisions. In this paper, we propose to formulate the harvest planning with growth uncertainty due to climate change problem as a multistage stochastic optimization problem and use sample average approximation (SAA) as a tool for finding the best set of forest units that should be harvested in the first period e

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40

Li, Tao. "Asymptotically Unbiased Average Consensus Under Measurement Noises and Fixed Topologies." IFAC Proceedings Volumes 41, no. 2 (2008): 2867–73. http://dx.doi.org/10.3182/20080706-5-kr-1001.00483.

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41

Gao, Xin, and Chun Xue. "Ridge Estimation for Uncertain Moving Average Model Under Imprecise Observations." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 30, no. 02 (2022): 171–87. http://dx.doi.org/10.1142/s0218488522500076.

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By observing and studying the time series, looking for the law of its change and development, and predicting its future trend is time series analysis. The observation data in traditional time series are accurate, but the statistical data we get in actual observations such as carbon emissions and temperature are often inaccurate. We regard this type of imprecise observation as uncertain variables, model it with the uncertain moving average model (UMA), and estimate the parameters in the UMA model with the ridge method. Then through the residual analysis of the fitted model, we acquire the forec

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42

Rothe, Christoph. "Robust Confidence Intervals for Average Treatment Effects Under Limited Overlap." Econometrica 85, no. 2 (2017): 645–60. http://dx.doi.org/10.3982/ecta13141.

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43

Baek, Jong-Il, Tae-Sung Kim, and Han-Ying Liang. "On the convergence of moving average processes under dependent conditions." Australian New Zealand Journal of Statistics 45, no. 3 (2003): 331–42. http://dx.doi.org/10.1111/1467-842x.00287.

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44

Chen, P., T. C. Hu, and A. Volodin. "Limiting behaviour of moving average processes under negative association assumption." Theory of Probability and Mathematical Statistics 77 (2008): 165–76. http://dx.doi.org/10.1090/s0094-9000-09-00755-8.

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45

Zhang, Dingchen, and Steve Hranilovic. "Bandlimited Optical Intensity Modulation Under Average and Peak Power Constraints." IEEE Transactions on Communications 64, no. 9 (2016): 3820–30. http://dx.doi.org/10.1109/tcomm.2016.2592519.

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46

Goken, Cagri, Berkan Dulek, and Sinan Gezici. "Optimal Stochastic Signaling Under Average Power and Bit Rate Constraints." IEEE Transactions on Communications 66, no. 12 (2018): 6028–39. http://dx.doi.org/10.1109/tcomm.2018.2864970.

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47

Baig, M. R., and M. S. Garawi. "Investigation of movement of average solute concentration under irradiation environment." Radiation Effects and Defects in Solids 153, no. 3 (2001): 281–88. http://dx.doi.org/10.1080/10420150108211845.

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48

Baig, M. R., and M. S. Garawi. "Investigation of movement of average solute concentration under irradiation environment." Radiation Effects and Defects in Solids 159, no. 4 (2004): 203–8. http://dx.doi.org/10.1080/10420150310001618188.

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49

Zhao, Yun, Yongluo Cao, and Jungchao Ban. "The Hausdorff dimension of average conformal repellers under random perturbation." Nonlinearity 22, no. 10 (2009): 2405–16. http://dx.doi.org/10.1088/0951-7715/22/10/005.

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50

Imbens, Guido W. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review." Review of Economics and Statistics 86, no. 1 (2004): 4–29. http://dx.doi.org/10.1162/003465304323023651.

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