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Статті в журналах з теми "Sharp bounds"

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Автор: Grafiati

Опубліковано: 7 липня 2024

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1

Blundell, Richard, Martin Browning, Laurens Cherchye, Ian Crawford, Bram De Rock, and Frederic Vermeulen. "Sharp for SARP: Nonparametric Bounds on Counterfactual Demands." American Economic Journal: Microeconomics 7, no. 1 (February 1, 2015): 43–60. http://dx.doi.org/10.1257/mic.20130150.

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Sharp nonparametric bounds are derived for counterfactual demands and Hicksian compensating and equivalent variations. These “i-bounds” refine and extend earlier results of Blundell, Browning, and Crawford (2008). We show that their bounds are sharp under the Weak Axiom of Revealed Preference (WARP) since they do not require transitivity. The new bounds are sharp under the Strong Axiom of Revealed Preference (SARP). By requiring transitivity they can be used to bound welfare measures. The new bounds on welfare measures are shown to be operationalized through algorithms that are easy to implement. (JEL D04, D11)

2

Hytönen, Tuomas, and Carlos Pérez. "Sharp weighted bounds involvingA∞." Analysis & PDE 6, no. 4 (August 21, 2013): 777–818. http://dx.doi.org/10.2140/apde.2013.6.777.

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3

Armitage, D. H., and Ü. Kuran. "Sharp Bounds for Harmonic Polynomials." Journal of the London Mathematical Society s2-42, no. 3 (December 1990): 475–88. http://dx.doi.org/10.1112/jlms/s2-42.3.475.

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4

Liu, Jingbo, Mohammad Hossein Yassaee, and Sergio Verdu. "Sharp Bounds for Mutual Covering." IEEE Transactions on Information Theory 65, no. 12 (December 2019): 8067–83. http://dx.doi.org/10.1109/tit.2019.2919720.

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5

Kim, Minkyun, and C. J. Neugebauer. "Sharp bounds for integral means." Journal of Mathematical Analysis and Applications 275, no. 2 (November 2002): 575–85. http://dx.doi.org/10.1016/s0022-247x(02)00255-x.

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6

Guo, Bai-Ni, and Feng Qi. "Sharp bounds for harmonic numbers." Applied Mathematics and Computation 218, no. 3 (October 2011): 991–95. http://dx.doi.org/10.1016/j.amc.2011.01.089.

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7

Yang, Zhen-Hang, Yu-Ming Chu, and Xiao-Hui Zhang. "Sharp bounds for psi function." Applied Mathematics and Computation 268 (October 2015): 1055–63. http://dx.doi.org/10.1016/j.amc.2015.07.012.

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8

Brown, Mark. "Sharp bounds for NBUE distributions." Annals of Operations Research 208, no. 1 (May 8, 2012): 245–50. http://dx.doi.org/10.1007/s10479-012-1151-0.

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9

Ciucu, Florin, Sima Mehri, and Amr Rizk. "On Ultra-Sharp Queueing Bounds." ACM SIGMETRICS Performance Evaluation Review 51, no. 2 (September 28, 2023): 27–29. http://dx.doi.org/10.1145/3626570.3626581.

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Martingale-based techniques render sharp bounds in several queueing scenarios, but mainly in heavy-traffic and subject to the degree of burstiness. We present a related technique to render ultra-sharp bounds across all utilization levels and for various degrees of burstiness.

10

Bovier, Anton. "Sharp upper bounds on perfect retrieval in the Hopfield model." Journal of Applied Probability 36, no. 3 (September 1999): 941–50. http://dx.doi.org/10.1239/jap/1032374647.

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We prove a sharp upper bound on the number of patterns that can be stored in the Hopfield model if the stored patterns are required to be fixed points of the gradient dynamics. We also show corresponding bounds on the one-step convergence of the sequential gradient dynamics. The bounds coincide with the known lower bounds and confirm the heuristic expectations. The proof is based on a crucial idea of Loukianova (1997) using the negative association properties of some random variables arising in the analysis.

11

Bovier, Anton. "Sharp upper bounds on perfect retrieval in the Hopfield model." Journal of Applied Probability 36, no. 03 (September 1999): 941–50. http://dx.doi.org/10.1017/s0021900200017708.

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We prove a sharp upper bound on the number of patterns that can be stored in the Hopfield model if the stored patterns are required to be fixed points of the gradient dynamics. We also show corresponding bounds on the one-step convergence of the sequential gradient dynamics. The bounds coincide with the known lower bounds and confirm the heuristic expectations. The proof is based on a crucial idea of Loukianova (1997) using the negative association properties of some random variables arising in the analysis.

12

Albritton, Dallas, and Nicola De Nitti. "Sharp bounds on enstrophy growth for viscous scalar conservation laws." Nonlinearity 36, no. 12 (November 16, 2023): 7142–48. http://dx.doi.org/10.1088/1361-6544/ad073f.

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Abstract We prove sharp bounds on the enstrophy growth in viscous scalar conservation laws. The upper bound is, up to a prefactor, the enstrophy created by the steepest viscous shock admissible by the L ∞ and total variation bounds and viscosity. This answers a conjecture by Ayala and Protas (2011 Physica D 240 1553–63), based on numerical evidence, for the viscous Burgers equation.

13

DATCHEV, KIRIL, SEMYON DYATLOV, and MACIEJ ZWORSKI. "Sharp polynomial bounds on the number of Pollicott–Ruelle resonances." Ergodic Theory and Dynamical Systems 34, no. 4 (March 11, 2013): 1168–83. http://dx.doi.org/10.1017/etds.2013.3.

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AbstractWe give a sharp polynomial bound on the number of Pollicott–Ruelle resonances. These resonances, which are complex numbers in the lower half-plane, appear in expansions of correlations for Anosov contact flows. The bounds follow the tradition of upper bounds on the number of scattering resonances and improve a recent bound of Faure and Sjöstrand. The complex scaling method used in scattering theory is replaced by an approach using exponentially weighted spaces introduced by Helffer and Sjöstrand in scattering theory and by Faure and Sjöstrand in the theory of Anosov flows.

14

Peña, Jose M. "Simple yet sharp sensitivity analysis for unmeasured confounding." Journal of Causal Inference 10, no. 1 (January 1, 2022): 1–17. http://dx.doi.org/10.1515/jci-2021-0041.

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Abstract We present a method for assessing the sensitivity of the true causal effect to unmeasured confounding. The method requires the analyst to set two intuitive parameters. Otherwise, the method is assumption free. The method returns an interval that contains the true causal effect and whose bounds are arbitrarily sharp, i.e., practically attainable. We show experimentally that our bounds can be tighter than those obtained by the method of Ding and VanderWeele, which, moreover, requires to set one more parameter than our method. Finally, we extend our method to bound the natural direct and indirect effects when there are measured mediators and unmeasured exposure–outcome confounding.

15

Spokoiny, V., and M. Zhilova. "Sharp deviation bounds for quadratic forms." Mathematical Methods of Statistics 22, no. 2 (April 2013): 100–113. http://dx.doi.org/10.3103/s1066530713020026.

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16

Laugesen, Richard, and Bartłomiej Siudeja. "Sharp spectral bounds on starlike domains." Journal of Spectral Theory 4, no. 2 (2014): 309–47. http://dx.doi.org/10.4171/jst/71.

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17

Mulas, R. "Sharp Bounds for the Largest Eigenvalue." Mathematical Notes 109, no. 1-2 (January 2021): 102–9. http://dx.doi.org/10.1134/s0001434621010120.

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18

Macris, N. "Sharp Bounds on Generalized EXIT Functions." IEEE Transactions on Information Theory 53, no. 7 (July 2007): 2365–75. http://dx.doi.org/10.1109/tit.2007.899536.

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19

Siudeja, Bartłomiej. "Sharp bounds for eigenvalues of triangles." Michigan Mathematical Journal 55, no. 2 (August 2007): 243–54. http://dx.doi.org/10.1307/mmj/1187646992.

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20

Stefanov, Plamen. "Quasimodes and resonances: Sharp lower bounds." Duke Mathematical Journal 99, no. 1 (July 1999): 75–92. http://dx.doi.org/10.1215/s0012-7094-99-09903-9.

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21

Simic, Slavko. "Sharp global bounds for Jensen's inequality." Rocky Mountain Journal of Mathematics 41, no. 6 (December 2011): 2021–31. http://dx.doi.org/10.1216/rmj-2011-41-6-2021.

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22

Miyazaki, Chikashi. "Sharp bounds on Castelnuovo-Mumford regularity." Transactions of the American Mathematical Society 352, no. 4 (October 21, 1999): 1675–86. http://dx.doi.org/10.1090/s0002-9947-99-02380-6.

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23

Ciucu, Florin, Felix Poloczek, and Jens Schmitt. "Sharp bounds in stochastic network calculus." ACM SIGMETRICS Performance Evaluation Review 41, no. 1 (June 14, 2013): 367–68. http://dx.doi.org/10.1145/2494232.2465746.

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24

Oymak, Samet, and Babak Hassibi. "Sharp MSE Bounds for Proximal Denoising." Foundations of Computational Mathematics 16, no. 4 (October 6, 2015): 965–1029. http://dx.doi.org/10.1007/s10208-015-9278-4.

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25

Mortici, Cristinel. "Sharp bounds of the Landau constants." Mathematics of Computation 80, no. 274 (May 1, 2011): 1011. http://dx.doi.org/10.1090/s0025-5718-2010-02428-7.

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26

Segura, Javier. "Sharp bounds for cumulative distribution functions." Journal of Mathematical Analysis and Applications 436, no. 2 (April 2016): 748–63. http://dx.doi.org/10.1016/j.jmaa.2015.12.024.

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27

Bellazzini, Jacopo, Marco Ghimenti, and Tohru Ozawa. "Sharp lower bounds for Coulomb energy." Mathematical Research Letters 23, no. 3 (2016): 621–32. http://dx.doi.org/10.4310/mrl.2016.v23.n3.a2.

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28

Cordero, Elena, and Fabio Nicola. "Sharp Integral Bounds for Wigner Distributions." International Mathematics Research Notices 2018, no. 6 (December 27, 2016): 1779–807. http://dx.doi.org/10.1093/imrn/rnw250.

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29

Hassani, Mehdi, and Anthony Sofo. "Sharp Bounds for the Constant e." Vietnam Journal of Mathematics 43, no. 3 (January 4, 2015): 629–33. http://dx.doi.org/10.1007/s10013-014-0114-y.

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30

Alzer, H. "Sharp bounds for the Bernoulli numbers." Archiv der Mathematik 74, no. 3 (March 1, 2000): 207–11. http://dx.doi.org/10.1007/s000130050432.

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31

Chen, Chao-Ping. "Sharp bounds for the Landau constants." Ramanujan Journal 31, no. 3 (December 12, 2012): 301–13. http://dx.doi.org/10.1007/s11139-012-9436-0.

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32

Gao, Guilian, and Fayou Zhao. "Sharp weak bounds for Hausdorff operators." Analysis Mathematica 41, no. 3 (September 2015): 163–73. http://dx.doi.org/10.1007/s10476-015-0204-4.

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33

Beauzamy, Bernard, Vilmar Trevisan, and Paul S. Wang. "Polynomial Factorization: Sharp Bounds, Efficient Algorithms." Journal of Symbolic Computation 15, no. 4 (April 1993): 393–413. http://dx.doi.org/10.1006/jsco.1993.1028.

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34

Brown, Mark. "Sharp bounds for exponential approximations under a hazard rate upper bound." Journal of Applied Probability 52, no. 03 (September 2015): 841–50. http://dx.doi.org/10.1017/s0021900200113476.

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Consider an absolutely continuous distribution on [0, ∞) with finite meanμand hazard rate functionh(t) ≤bfor allt. Forbμclose to 1, we would expectFto be approximately exponential. In this paper we obtain sharp bounds for the Kolmogorov distance betweenFand an exponential distribution with meanμ, as well as betweenFand an exponential distribution with failure rateb. We apply these bounds to several examples. Applications are presented to geometric convolutions, birth and death processes, first-passage times, and to decreasing mean residual life distributions.

35

Brown, Mark. "Sharp bounds for exponential approximations under a hazard rate upper bound." Journal of Applied Probability 52, no. 3 (September 2015): 841–50. http://dx.doi.org/10.1239/jap/1445543850.

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Consider an absolutely continuous distribution on [0, ∞) with finite mean μ and hazard rate function h(t) ≤ b for all t. For bμ close to 1, we would expect F to be approximately exponential. In this paper we obtain sharp bounds for the Kolmogorov distance between F and an exponential distribution with mean μ, as well as between F and an exponential distribution with failure rate b. We apply these bounds to several examples. Applications are presented to geometric convolutions, birth and death processes, first-passage times, and to decreasing mean residual life distributions.

36

Puccetti, Giovanni, and Ludger Rüschendorf. "Sharp Bounds for Sums of Dependent Risks." Journal of Applied Probability 50, no. 1 (March 2013): 42–53. http://dx.doi.org/10.1239/jap/1363784423.

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Sharp tail bounds for the sum of d random variables with given marginal distributions and arbitrary dependence structure have been known since Makarov (1981) and Rüschendorf (1982) for d=2 and, in some examples, for d≥3. Based on a duality result, dual bounds have been introduced in Embrechts and Puccetti (2006b). In the homogeneous case, F1=···=Fn, with monotone density, sharp tail bounds were recently found in Wang and Wang (2011). In this paper we establish the sharpness of the dual bounds in the homogeneous case under general conditions which include, in particular, the case of monotone densities and concave densities. We derive the corresponding optimal couplings and also give an effective method to calculate the sharp bounds.

37

Puccetti, Giovanni, and Ludger Rüschendorf. "Sharp Bounds for Sums of Dependent Risks." Journal of Applied Probability 50, no. 01 (March 2013): 42–53. http://dx.doi.org/10.1017/s0021900200013103.

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Sharp tail bounds for the sum of d random variables with given marginal distributions and arbitrary dependence structure have been known since Makarov (1981) and Rüschendorf (1982) for d=2 and, in some examples, for d≥3. Based on a duality result, dual bounds have been introduced in Embrechts and Puccetti (2006b). In the homogeneous case, F 1=···=F n , with monotone density, sharp tail bounds were recently found in Wang and Wang (2011). In this paper we establish the sharpness of the dual bounds in the homogeneous case under general conditions which include, in particular, the case of monotone densities and concave densities. We derive the corresponding optimal couplings and also give an effective method to calculate the sharp bounds.

38

From, Steven G. "SOME NEW RELIABILITY BOUNDS FOR SUMS OF NBUE RANDOM VARIABLES." Probability in the Engineering and Informational Sciences 25, no. 1 (November 2, 2010): 83–102. http://dx.doi.org/10.1017/s0269964810000264.

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In this article, we discuss some new upper and lower bounds for the survivor function of the sum of n independent random variables each of which has an NBUE (new better than used in expectation) distribution. In some cases, only the means of the random variables are assumed known. These bounds are compared to the sharp bounds given in Cheng and Lam [6], which requires both means and variances known. Although the new bounds are not sharp, they often produce better upper bounds for the survivor function in the extreme right tail of many NBUE lifetime distributions, an important special case in applications. Moreover, a lower bound exists in one case not handled by the lower bounds of Theorem 3 in Cheng and Lam [6]. Numerical studies are presented along with theoretical discussions.

39

Osękowski, Adam. "Sharp Localized Inequalities for Fourier Multipliers." Canadian Journal of Mathematics 66, no. 6 (December 1, 2014): 1358–81. http://dx.doi.org/10.4153/cjm-2013-050-5.

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AbstractIn this paper we study sharp localized Lq → Lp estimates for Fourier multipliers resulting from modulation of the jumps of Lévy processes. The proofs of these estimates rest on probabilistic methods and exploit related sharp bounds for differentially subordinated martingales, which are of independent interest. The lower bounds for the constants involve the analysis of laminates, a family of certain special probability measures on 2×2 matrices. As an application, we obtain new sharp bounds for the real and imaginary parts of the Beurling–Ahlfors operator.

40

Ali, Rashid, Mohsan Raza, and Teodor Bulboacă. "Sharp Coefficient Bounds for Starlike Functions Associated with Cosine Function." Axioms 13, no. 7 (June 29, 2024): 442. http://dx.doi.org/10.3390/axioms13070442.

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Let Scos* denote the class of normalized analytic functions f in the open unit disk D satisfying the subordination zf′(z)f(z)≺cosz. In the first result of this article, we find the sharp upper bounds for the initial coefficients a3, a4 and a5 and the sharp upper bound for module of the Hankel determinant |H2,3(f)| for the functions from the class Scos*. The next section deals with the sharp upper bounds of the logarithmic coefficients γ3 and γ4. Then, in addition, we found the sharp upper bound for H2,2Ff/2. To obtain these results we utilized the very useful and appropriate Lemma 2.4 of N.E. Cho et al. [Filomat 34(6) (2020), 2061–2072], which gave a most accurate description for the first five coefficients of the functions from the Carathéodory’s functions class, and provided a technique for finding the maximum value of a three-variable function on a closed cuboid. All the maximum found values were checked by using MAPLE™ 2016 computer software, and we also found the extremal functions in each case. All of our most recent results are the best ones and give sharp versions of those recently published in [Hacet. J. Math. Stat. 52, 596–618, 2023].

41

Chouikha, Abd Raouf. "Sharp inequalities related to Wilker results." Open Journal of Mathematical Sciences 7, no. 1 (March 31, 2023): 19–34. http://dx.doi.org/10.30538/oms2023.0196.

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In this paper, we interested in Wilker inequalities. We provide finer bounds than known previous. Moreover, bounds are obtained for the following trigonometric function $$g_n(x) = \left(\frac{\sin(x)}{x}\right)^2 \left( 1 - \frac{2\left(\frac{2 x}{\pi}\right)^{2n+2}}{1-(\frac{2x}{\pi})^2}\right) +\frac{\tan(x)}{x}, \ n\geq 0.$$

42

CHEN, YA-HONG, RONG-YING PAN, and XIAO-DONG ZHANG. "TWO SHARP UPPER BOUNDS FOR THE SIGNLESS LAPLACIAN SPECTRAL RADIUS OF GRAPHS." Discrete Mathematics, Algorithms and Applications 03, no. 02 (June 2011): 185–91. http://dx.doi.org/10.1142/s1793830911001152.

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The signless Laplacian matrix of a graph is the sum of its degree diagonal and adjacency matrices. In this paper, we present a sharp upper bound for the spectral radius of the adjacency matrix of a graph. Then this result and other known results are used to obtain two new sharp upper bounds for the signless Laplacian spectral radius. Moreover, the extremal graphs which attain an upper bound are characterized.

43

Raza, M., D. K. Thomas, and A. Riaz. "Coefficient estimates for starlike and convex functions related to sigmoid functions." Ukrains’kyi Matematychnyi Zhurnal 75, no. 5 (May 24, 2023): 683–97. http://dx.doi.org/10.37863/umzh.v75i5.7093.

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UDC 517.5 We give sharp coefficient bounds for starlike and convex functions related to modified sigmoid functions. We also provide some sharp coefficients bounds for the inverse functions and sharp bounds for the initial logarithmic coefficients and some coefficient differences.

44

Kılıçer, Pınar, Elisa Lorenzo García, and Marco Streng. "Primes Dividing Invariants of CM Picard Curves." Canadian Journal of Mathematics 72, no. 2 (May 7, 2019): 480–504. http://dx.doi.org/10.4153/s0008414x18000111.

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AbstractWe give a bound on the primes dividing the denominators of invariants of Picard curves of genus 3 with complex multiplication. Unlike earlier bounds in genus 2 and 3, our bound is based, not on bad reduction of curves, but on a very explicit type of good reduction. This approach simultaneously yields a simplification of the proof and much sharper bounds. In fact, unlike all previous bounds for genus 3, our bound is sharp enough for use in explicit constructions of Picard curves.

45

Bañuelos, Rodrigo, and Adam Osekowski. "Martingales and sharp bounds for Fourier multipliers." Annales Academiae Scientiarum Fennicae Mathematica 37 (February 2012): 251–63. http://dx.doi.org/10.5186/aasfm.2012.3710.

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46

Dunster, T. M., A. Gil, and J. Segura. "Sharp error bounds for turning point expansions." Journal of Classical Analysis, no. 1 (2021): 49–81. http://dx.doi.org/10.7153/jca-2021-18-05.

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47

Xia, Fang-Li, Wei-Mao Qian, Shu-Bo Chen, and Yu-Ming Chu. "Sharp bounds for Neuman means with applications." Journal of Nonlinear Sciences and Applications 09, no. 05 (May 20, 2016): 2031–38. http://dx.doi.org/10.22436/jnsa.009.05.09.

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48

O'Neill, Bruce. "Sharp bounds for derivatives of univalent functions." Complex Variables, Theory and Application: An International Journal 5, no. 2-4 (March 1986): 281–88. http://dx.doi.org/10.1080/17476938608814148.

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49

Prékopa, András. "Sharp Bounds on Probabilities Using Linear Programming." Operations Research 38, no. 2 (April 1990): 227–39. http://dx.doi.org/10.1287/opre.38.2.227.

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50

Alzahrani, Faris, and Ahmed Salem. "Sharp bounds for the Lambert W function." Integral Transforms and Special Functions 29, no. 12 (October 2, 2018): 971–78. http://dx.doi.org/10.1080/10652469.2018.1528247.

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