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Journal articles on the topic 'Survival probability'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 June 2025

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1

Zarate-Herrada, David A., Lea F. Santos, and E. Jonathan Torres-Herrera. "Generalized Survival Probability." Entropy 25, no. 2 (2023): 205. http://dx.doi.org/10.3390/e25020205.

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Survival probability measures the probability that a system taken out of equilibrium has not yet transitioned from its initial state. Inspired by the generalized entropies used to analyze nonergodic states, we introduce a generalized version of the survival probability and discuss how it can assist in studies of the structure of eigenstates and ergodicity.

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2

Casati, Giulio, Italo Guarneri, and Giulio Maspero. "Fractal Survival Probability Fluctuations." Physical Review Letters 84, no. 1 (2000): 63–66. http://dx.doi.org/10.1103/physrevlett.84.63.

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3

Wadia, Reena. "Survival probability of autotransplanted teeth." British Dental Journal 226, no. 12 (2019): 950. http://dx.doi.org/10.1038/s41415-019-0466-5.

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4

Crawford, T. O. "Survival probability in ataxia telangiectasia." Archives of Disease in Childhood 91, no. 7 (2005): 610–11. http://dx.doi.org/10.1136/adc.2006.094268.

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5

Stockman, J. A. "Survival probability in ataxia telangiectasia." Yearbook of Pediatrics 2008 (January 2008): 364–66. http://dx.doi.org/10.1016/s0084-3954(08)70471-8.

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6

Luo, Jian-hua. "Survival probability and ruin probability of a risk model." Applied Mathematics-A Journal of Chinese Universities 23, no. 3 (2008): 256–64. http://dx.doi.org/10.1007/s11766-008-1916-z.

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7

Hanada, Toshiya, Hiroshi Hirayama, Atsushi Oishi, Yosuke Tanaka, and Tetsuo Yasaka. "Survival Probability Assessment of Space Tethers." JOURNAL OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES 54, no. 630 (2006): 295–304. http://dx.doi.org/10.2322/jjsass.54.295.

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8

García-Calderón, Gastón, Verónica Riquer, and Roberto Romo. "Survival probability of a single resonance." Journal of Physics A: Mathematical and General 34, no. 19 (2001): 4155–65. http://dx.doi.org/10.1088/0305-4470/34/19/313.

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9

Levin, E., A. D. Martin, and M. G. Ryskin. "Survival probability of large rapidity gaps." Journal of Physics G: Nuclear and Particle Physics 25, no. 7 (1999): 1507–10. http://dx.doi.org/10.1088/0954-3899/25/7/336.

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10

Dettmann, Carl P., and Mohammed R. Rahman. "Survival probability for open spherical billiards." Chaos: An Interdisciplinary Journal of Nonlinear Science 24, no. 4 (2014): 043130. http://dx.doi.org/10.1063/1.4900776.

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11

Biau, David Jean, Aur??lien Latouche, and Rapha??l Porcher. "Competing Events Influence Estimated Survival Probability." Clinical Orthopaedics and Related Research 462 (September 2007): 229–33. http://dx.doi.org/10.1097/blo.0b013e3180986753.

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12

Gotsman, E. "Survival probability in hadron-hadron interactions." Nuclear Physics B - Proceedings Supplements 79, no. 1-3 (1999): 389–92. http://dx.doi.org/10.1016/s0920-5632(99)00732-x.

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13

Dettmann, Carl P., and Orestis Georgiou. "Survival probability for the stadium billiard." Physica D: Nonlinear Phenomena 238, no. 23-24 (2009): 2395–403. http://dx.doi.org/10.1016/j.physd.2009.09.019.

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14

Luo, Shunlong. "On survival probability of quantum states." Journal of Physics A: Mathematical and General 38, no. 13 (2005): 2991–95. http://dx.doi.org/10.1088/0305-4470/38/13/012.

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15

Gajek, Lesław, and Dariusz Zagrodny. "Reinsurance Arrangements Maximizing Insurer's Survival Probability." Journal of Risk and Insurance 71, no. 3 (2004): 421–35. http://dx.doi.org/10.1111/j.0022-4367.2004.00097.x.

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16

Yang, Grace L. "Cell survival probability under ionizing radiation." Mathematical Biosciences 112, no. 2 (1992): 305–17. http://dx.doi.org/10.1016/0025-5564(92)90029-v.

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17

Muga, J. G., G. W. Wei, and R. F. Snider. "Survival Probability for the Yamaguchi Potential." Annals of Physics 252, no. 2 (1996): 336–56. http://dx.doi.org/10.1006/aphy.1996.0135.

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18

Hemyari, Parichehr. "ROBUSTNESS OF THE QUARTILES OF SURVIVAL TIME AND SURVIVAL PROBABILITY." Journal of Biopharmaceutical Statistics 10, no. 3 (2000): 299–318. http://dx.doi.org/10.1081/bip-100102496.

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19

Palmer, Duncan, and Niel Krige. "The financial survival probability of living annuitants." Journal of Economic and Financial Sciences 6, no. 1 (2013): 167–78. http://dx.doi.org/10.4102/jef.v6i1.282.

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This study addresses the question of how long a given amount of capital will be able to fund a living annuitant if the following five parameters are known: expected retirement duration (i.e. years between date of retirement and date of death), return on investment, inflation, annual withdrawal amount and initial capital amount available. A model (the Pension Model) that graphically depicts the relationship between these parameters was developed. This model facilitates retirement planning by showing how retirement duration and withdrawal rates change the financial “Survival Probability” (SP), which is the probability of having enough capital to maintain a desired withdrawal rate for the expected retirement duration. The underlying model is based on long-term historical investment yields of equities, bonds and cash in South Africa using Monte Carlo simulation with Cholesky factorisation.

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20

Buci, Skender, and Agim Kukeli. "Survival probability in patients with liver trauma." International Journal of Health Care Quality Assurance 29, no. 7 (2016): 778–85. http://dx.doi.org/10.1108/ijhcqa-04-2016-0045.

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Purpose – The purpose of this paper is to assess the survival probability among patients with liver trauma injury using the anatomical and psychological scores of conditions, characteristics and treatment modes. Design/methodology/approach – A logistic model is used to estimate 173 patients’ survival probability. Data are taken from patient records. Only emergency room patients admitted to University Hospital of Trauma (former Military Hospital) in Tirana are included. Data are recorded anonymously, preserving the patients’ privacy. Findings – When correctly predicted, the logistic models show that survival probability varies from 70.5 percent up to 95.4 percent. The degree of trauma injury, trauma with liver and other organs, total days the patient was hospitalized, and treatment method (conservative vs intervention) are statistically important in explaining survival probability. Practical implications – The study gives patients, their relatives and physicians ample and sound information they can use to predict survival chances, the best treatment and resource management. Originality/value – This study, which has not been done previously, explores survival probability, success probability for conservative and non-conservative treatment, and success probability for single vs multiple injuries from liver trauma.

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21

Pietronero, L. "Survival Probability for Kinetic Self-Avoiding Walks." Physical Review Letters 55, no. 19 (1985): 2025–27. http://dx.doi.org/10.1103/physrevlett.55.2025.

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22

Bhat, C. M., P. C. Bhat, M. Paterno, and H. B. Prosper. "Study of the Solar Neutrino Survival Probability." Physical Review Letters 81, no. 23 (1998): 5056–59. http://dx.doi.org/10.1103/physrevlett.81.5056.

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23

Redner, S. "Survival probability in a random velocity field." Physical Review E 56, no. 5 (1997): 4967–72. http://dx.doi.org/10.1103/physreve.56.4967.

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24

Poltoratski, Alexei. "Survival Probability¶in Rank-One Perturbation Problems." Communications in Mathematical Physics 223, no. 1 (2001): 205–22. http://dx.doi.org/10.1007/s002200100541.

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25

Cole, Stephen R., and Miguel A. Hernán. "Adjusted survival curves with inverse probability weights." Computer Methods and Programs in Biomedicine 75, no. 1 (2004): 45–49. http://dx.doi.org/10.1016/j.cmpb.2003.10.004.

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26

Militello, B. "Governing Survival Probability to Distill Quantum States." Optics and Spectroscopy 99, no. 3 (2005): 438. http://dx.doi.org/10.1134/1.2055940.

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27

Ben-Haim, Yakov. "Robust satisficing and the probability of survival." International Journal of Systems Science 45, no. 1 (2012): 3–19. http://dx.doi.org/10.1080/00207721.2012.684906.

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28

Liu, Sheen, Peter Woodlock, Howard Qi, and Yan Alice Xie. "Cash Reserve and Venture Business Survival Probability." Journal of Entrepreneurial Finance 11, no. 3 (2006): 123–36. http://dx.doi.org/10.57229/2373-1761.1042.

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29

Kim, Jeong-Hoon, Yong-Ki Ma, and Chan Yeol Park. "Joint survival probability via truncated invariant copula." Chaos, Solitons & Fractals 85 (April 2016): 68–76. http://dx.doi.org/10.1016/j.chaos.2016.01.012.

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30

Fletcher, R. S. "The gap survival probability and diffractive dissociation." Physics Letters B 320, no. 3-4 (1994): 373–76. http://dx.doi.org/10.1016/0370-2693(94)90672-6.

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31

Mansur-Azzam, Nura, Zeinab Hosseinidoust, Su Gyeong Woo, Renata Vyhnalkova, Adi Eisenberg, and Theo G. M. van de Ven. "Bacteria survival probability in bactericidal filter paper." Colloids and Surfaces B: Biointerfaces 117 (May 2014): 383–88. http://dx.doi.org/10.1016/j.colsurfb.2014.03.011.

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32

Kennedy, Alexander W., Maurie Markman, Charles V. Biscotti, Jonathan D. Emery, and Lisa A. Rybicki. "Survival Probability in Ovarian Clear Cell Adenocarcinoma." Gynecologic Oncology 74, no. 1 (1999): 108–14. http://dx.doi.org/10.1006/gyno.1999.5445.

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33

Ren, Kan, Jiarui Qin, Lei Zheng, et al. "Deep Recurrent Survival Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4798–805. http://dx.doi.org/10.1609/aaai.v33i01.33014798.

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Survival analysis is a hotspot in statistical research for modeling time-to-event information with data censorship handling, which has been widely used in many applications such as clinical research, information system and other fields with survivorship bias. Many works have been proposed for survival analysis ranging from traditional statistic methods to machine learning models. However, the existing methodologies either utilize counting-based statistics on the segmented data, or have a pre-assumption on the event probability distribution w.r.t. time. Moreover, few works consider sequential patterns within the feature space. In this paper, we propose a Deep Recurrent Survival Analysis model which combines deep learning for conditional probability prediction at finegrained level of the data, and survival analysis for tackling the censorship. By capturing the time dependency through modeling the conditional probability of the event for each sample, our method predicts the likelihood of the true event occurrence and estimates the survival rate over time, i.e., the probability of the non-occurrence of the event, for the censored data. Meanwhile, without assuming any specific form of the event probability distribution, our model shows great advantages over the previous works on fitting various sophisticated data distributions. In the experiments on the three realworld tasks from different fields, our model significantly outperforms the state-of-the-art solutions under various metrics.

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34

Kamal, Oussouadi, and Cherkaoui Kenza. "Survival dynamics of SMES supported by credit guarantee schemes: Insights from Morocco." Banks and Bank Systems 19, no. 1 (2024): 86–98. http://dx.doi.org/10.21511/bbs.19(1).2024.08.

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The aim of this study is to assess the viability of SMEs that had benefited from bank loans backed by credit guarantee schemes. A quantitative approach has been adopted by the study. The sample comprised 398 Moroccan SMEs that had benefited from this type of financing, and the primary objective was to examine their survival over the ten years following the obtaining of these guarantees. Logistic regression was used to reflect several results. The results of the study highlight several factors influencing the probability of survival of these SMEs. Larger amounts of credit promote financial resilience and growth, thereby increasing the likelihood of business survival. Business profitability is a key factor in the likelihood of survival. Profitable businesses attract more investors and lenders, improving access to credit and increasing survival prospects. Contrary to some studies, high levels of debt do not appear to reduce the probability of survival. Similarly, repayment capacity showed no significant link with survival, suggesting the importance of other non-financial factors. Mature and well-considered management decision making is associated with a higher probability of survival. Well-thought-out decisions promote the long-term viability of businesses. Small SMEs also have a good chance of survival because of their rapid adaptability. A manager’s active participation in a company’s capital is linked to a higher probability of survival. This underlines the importance of the personal involvement of the manager and of solid governance.

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35

Muzaffar, Mahvish. "Conditional survival probability of patients with pancreatic cancer." Journal of Clinical Oncology 30, no. 15_suppl (2012): e14693-e14693. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.e14693.

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e14693 Background: Conditional survival gives more accurate estimate of survival probability for patients who have survived one or more years since initial diagnosis. The aim of this study was to analyze conditional survival probability for pancreatic cancer patients and impact of gender, age and extent of disease on conditional survival. Methods: Using the Surveillance, Epidemiology, and End Results database we analyzed 57,409 patients with pancreatic cancer diagnosed between 1990 and 2008. SEER*Stat: Version7.0.5 software was used to calculate conditional survival, defined as the calculated probability of survival having already survived a specified number of years from diagnosis, with sub-stratification by age(less than 65 yrs and above/equal 65yrs ), gender and stage summary (localized, regional and distant). Results: Conditional survival probability for patients with pancreatic cancer increased from 8.3% to 47.1% having survived one year after diagnosis. Conditional survival per gender increased from 8.5% to 49.8% in women and for men from 8.1% to 44.3%. Conditional survival probability per summary stage increased most for patients with localized disease (from 25.5% to 70.2%) as opposed to distant disease (3.2% to 37.0%). Age may have a slight impact on conditional survival probability, with increase from 12.5% to 50.7% in patient younger than 65 years, as opposed to from 5.7% to 42.3% in age group more than or equal to 65yrs. Conclusions: The expected 5-year conditional survival increases for pancreatic cancer patients who survive a number of years post diagnosis. The increase in conditional survival probability may be to a lesser extent than other cancers, but provides more useful longer term prognostic information.

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36

Vissani, Francesco. "Joint analysis of Borexino and SNO solar neutrino data and reconstruction of the survival probability." Nuclear Physics and Atomic Energy 18, no. 4 (2017): 303–12. http://dx.doi.org/10.15407/jnpae2017.04.303.

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37

Mi, Jie. "Maximization of a survival probability and its application." Journal of Applied Probability 31, no. 4 (1994): 1026–33. http://dx.doi.org/10.2307/3215326.

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When a mission is assigned, it often is the case that the component used to perform the task is required to work properly during the period of the mission time. In other words, the probability of the event that this component does not fail within the allowable mission time should be as large as possible. This problem is considered for the case when the lifetime of a component has a bathtub-shaped failure rate function, and it is found that burn-in procedure is beneficial. An application of this result to the problem of minimizing the mean number of failures in a given period of mission time is also considered.

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38

Kim, Dong Wook, and Byoung Gon Kim. "Korean Listed SMEs Ownership Structure and Survival Probability." Journal of Industrial Economics and Business 32, no. 6 (2019): 2317–35. http://dx.doi.org/10.22558/jieb.2019.12.32.6.2317.

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39

Kang, Yung-Gyung, and Jeong-Man Park. "Survival Probability of Quasi-Species under Environmental Changes." Journal of the Korean Physical Society 53, no. 2 (2008): 868–72. http://dx.doi.org/10.3938/jkps.53.868.

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40

Pozzoli, Gaia, and Benjamin De Bruyne. "Survival probability of random walks leaping over traps." Journal of Statistical Mechanics: Theory and Experiment 2021, no. 12 (2021): 123203. http://dx.doi.org/10.1088/1742-5468/ac3e6f.

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Abstract We consider one-dimensional discrete-time random walks (RWs) in the presence of finite size traps of length ℓ over which the RWs can jump. We study the survival probability of such RWs when the traps are periodically distributed and separated by a distance L. We obtain exact results for the mean first-passage time and the survival probability in the special case of a double-sided exponential jump distribution. While such RWs typically survive longer than if they could not leap over traps, their survival probability still decreases exponentially with the number of steps. The decay rate of the survival probability depends in a non-trivial way on the trap length ℓ and exhibits an interesting regime when ℓ → 0 as it tends to the ratio ℓ/L, which is reminiscent of strongly chaotic deterministic systems. We generalize our model to continuous-time RWs, where we introduce a power-law distributed waiting time before each jump. In this case, we find that the survival probability decays algebraically with an exponent that is independent of the trap length. Finally, we derive the diffusive limit of our model and show that, depending on the chosen scaling, we obtain either diffusion with uniform absorption, or diffusion with periodically distributed point absorbers.

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41

Li, Zenghu, and Yaping Zhu. "Survival probability for super-Brownian motion with absorption." Statistics & Probability Letters 186 (July 2022): 109460. http://dx.doi.org/10.1016/j.spl.2022.109460.

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42

Kiesel, Rüdiger, and Luitgard Veraart. "A note on the survival probability in CreditGrades." Journal of Credit Risk 4, no. 2 (2008): 65–74. http://dx.doi.org/10.21314/jcr.2008.070.

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43

Jiang, Jinglu, Congming Mu, Juan Peng, and Jinqiang Yang. "Real options maximizing survival probability under incomplete markets." Quantitative Finance 19, no. 11 (2019): 1921–31. http://dx.doi.org/10.1080/14697688.2019.1617891.

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44

Chen, Robert, Ilie Grigorescu, and Larry Shepp. "Maximizing the discounted survival probability in Vardi's casino." Stochastics 83, no. 4-6 (2011): 623–38. http://dx.doi.org/10.1080/17442508.2011.552980.

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45

MoGonigal, Michael D., John Cole, Donald R. Kauder, et al. "A NEW APPROACH TO PROBABILITY OF SURVIVAL SCORING." Journal of Trauma: Injury, Infection, and Critical Care 33, no. 1 (1992): 156. http://dx.doi.org/10.1097/00005373-199207000-00061.

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46

Mi, Jie. "Maximization of a survival probability and its application." Journal of Applied Probability 31, no. 04 (1994): 1026–33. http://dx.doi.org/10.1017/s002190020009954x.

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When a mission is assigned, it often is the case that the component used to perform the task is required to work properly during the period of the mission time. In other words, the probability of the event that this component does not fail within the allowable mission time should be as large as possible. This problem is considered for the case when the lifetime of a component has a bathtub-shaped failure rate function, and it is found that burn-in procedure is beneficial. An application of this result to the problem of minimizing the mean number of failures in a given period of mission time is also considered.

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47

Nan, Wang, Zhao En-Guang, Li Wen-Fei, et al. "Study of Survival Probability of Super Heavy Nuclei." Communications in Theoretical Physics 40, no. 2 (2003): 199–202. http://dx.doi.org/10.1088/0253-6102/40/2/199.

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48

Ma, Jian-Zhong. "Correlation Hole of Survival Probability and Level Statistics." Journal of the Physical Society of Japan 64, no. 11 (1995): 4059–63. http://dx.doi.org/10.1143/jpsj.64.4059.

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49

Gewaltig, Marc-Oliver, Markus Diesmann, and Ad Aertsen. "Cortical synfire-activity: Configuration space and survival probability." Neurocomputing 38-40 (June 2001): 621–26. http://dx.doi.org/10.1016/s0925-2312(01)00454-4.

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50

Wang, Guoqiao, and Inmaculada Aban. "Application of inverse probability weights in survival analysis." Journal of Nuclear Cardiology 22, no. 4 (2015): 611–13. http://dx.doi.org/10.1007/s12350-015-0157-9.

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