Relevant bibliographies by topics / Bayesian Sample size

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Bayesian Sample size'

Author: Grafiati
Published: 10 March 2023

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Journal articles on the topic "Bayesian Sample size"

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Nassar, M. M., S. M. Khamis, and S. S. Radwan. "On Bayesian sample size determination." Journal of Applied Statistics 38, no. 5 (May 2011): 1045–54. http://dx.doi.org/10.1080/02664761003758992.

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Pham-Gia, T., and N. Turkkan. "Sample Size Determination in Bayesian Analysis." Statistician 41, no. 4 (1992): 389. http://dx.doi.org/10.2307/2349003.

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Sobel, Marc, and Ibrahim Turkoz. "Bayesian blinded sample size re-estimation." Communications in Statistics - Theory and Methods 47, no. 24 (December 8, 2017): 5916–33. http://dx.doi.org/10.1080/03610926.2017.1404097.

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Wang, Ming-Dauh. "Sample Size Reestimation by Bayesian Prediction." Biometrical Journal 49, no. 3 (June 2007): 365–77. http://dx.doi.org/10.1002/bimj.200310273.

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Wang, Ming-Dauh. "Sample Size Reestimation by Bayesian Prediction." Biometrical Journal 49, no. 3 (June 2007): NA. http://dx.doi.org/10.1002/bimj.200510273.

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JOSEPH, LAWRENCE, ROXANE DU BERGER, and PATRICK BÉLISLE. "BAYESIAN AND MIXED BAYESIAN/LIKELIHOOD CRITERIA FOR SAMPLE SIZE DETERMINATION." Statistics in Medicine 16, no. 7 (April 15, 1997): 769–81. http://dx.doi.org/10.1002/(sici)1097-0258(19970415)16:7<769::aid-sim495>3.0.co;2-v.

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De Santis, Fulvio. "Sample Size Determination for Robust Bayesian Analysis." Journal of the American Statistical Association 101, no. 473 (March 2006): 278–91. http://dx.doi.org/10.1198/016214505000000510.

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Weiss, Robert. "Bayesian sample size calculations for hypothesis testing." Journal of the Royal Statistical Society: Series D (The Statistician) 46, no. 2 (July 1997): 185–91. http://dx.doi.org/10.1111/1467-9884.00075.

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Katsis, Athanassios, and Blaza Toman. "Bayesian sample size calculations for binomial experiments." Journal of Statistical Planning and Inference 81, no. 2 (November 1999): 349–62. http://dx.doi.org/10.1016/s0378-3758(99)00019-1.

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Clarke, B., and Ao Yuan. "Closed form expressions for Bayesian sample size." Annals of Statistics 34, no. 3 (June 2006): 1293–330. http://dx.doi.org/10.1214/009053606000000308.

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Dissertations / Theses on the topic "Bayesian Sample size"

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Cámara, Hagen Luis Tomás. "A consensus based Bayesian sample size criterion." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ64329.pdf.

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Cheng, Dunlei Stamey James D. "Topics in Bayesian sample size determination and Bayesian model selection." Waco, Tex. : Baylor University, 2007. http://hdl.handle.net/2104/5039.

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Islam, A. F. M. Saiful. "Loss functions, utility functions and Bayesian sample size determination." Thesis, Queen Mary, University of London, 2011. http://qmro.qmul.ac.uk/xmlui/handle/123456789/1259.

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This thesis consists of two parts. The purpose of the first part of the research is to obtain Bayesian sample size determination (SSD) using loss or utility function with a linear cost function. A number of researchers have studied the Bayesian SSD problem. One group has considered utility (loss) functions and cost functions in the SSD problem and others not. Among the former most of the SSD problems are based on a symmetrical squared error (SE) loss function. On the other hand, in a situation when underestimation is more serious than overestimation or vice-versa, then an asymmetric loss function should be used. For such a loss function how many observations do we need to take to estimate the parameter under study? We consider different types of asymmetric loss functions and a linear cost function for sample size determination. For the purposes of comparison, firstly we discuss the SSD for a symmetric squared error loss function. Then we consider the SSD under different types of asymmetric loss functions found in the literature. We also introduce a new bounded asymmetric loss function and obtain SSD under this loss function. In addition, to estimate a parameter following a particular model, we present some theoretical results for the optimum SSD problem under a particular choice of loss function. We also develop computer programs to obtain the optimum SSD where the analytic results are not possible. In the two parameter exponential family it is difficult to estimate the parameters when both are unknown. The aim of the second part is to obtain an optimum decision for the two parameter exponential family under the two parameter conjugate utility function. In this case we discuss Lindley’s (1976) optimum decision for one 6 parameter exponential family under the conjugate utility function for the one parameter exponential family and then extend the results to the two parameter exponential family. We propose a two parameter conjugate utility function and then lay out the approximation procedure to make decisions on the two parameters. We also offer a few examples, normal distribution, trinomial distribution and inverse Gaussian distribution and provide the optimum decisions on both parameters of these distributions under the two parameter conjugate utility function.
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M'lan, Cyr Emile. "Bayesian sample size calculations for cohort and case-control studies." Thesis, McGill University, 2002. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82923.

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Sample size determination is one of the most important statistical issues in the early stages of any investigation that anticipates statistical analyses.
In this thesis, we examine Bayesian sample size determination methodology for interval estimation. Four major epidemiological study designs, cohort, case-control, cross-sectional and matched pair are the focus. We study three Bayesian sample size criteria: the average length criterion (ALC), the average coverage criterion ( ACC) and the worst outcome criterion (WOC ) as well as various extensions of these criteria. In addition, a simple cost function is included as part of our sample size calculations for cohort and case-controls studies. We also examine the important design issue of the choice of the optimal ratio of controls per case in case-control settings or non-exposed to exposed in cohort settings.
The main difficulties with Bayesian sample size calculation problems are often at the computational level. Thus, this thesis is concerned, to a considerable extent, with presenting sample size methods that are computationally efficient.
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Banton, Dwaine Stephen. "A BAYESIAN DECISION THEORETIC APPROACH TO FIXED SAMPLE SIZE DETERMINATION AND BLINDED SAMPLE SIZE RE-ESTIMATION FOR HYPOTHESIS TESTING." Diss., Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/369007.

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Statistics
Ph.D.
This thesis considers two related problems that has application in the field of experimental design for clinical trials: • fixed sample size determination for parallel arm, double-blind survival data analysis to test the hypothesis of no difference in survival functions, and • blinded sample size re-estimation for the same. For the first problem of fixed sample size determination, a method is developed generally for testing of hypothesis, then applied particularly to survival analysis; for the second problem of blinded sample size re-estimation, a method is developed specifically for survival analysis. In both problems, the exponential survival model is assumed. The approach we propose for sample size determination is Bayesian decision theoretical, using explicitly a loss function and a prior distribution. The loss function used is the intrinsic discrepancy loss function introduced by Bernardo and Rueda (2002), and further expounded upon in Bernardo (2011). We use a conjugate prior, and investigate the sensitivity of the calculated sample sizes to specification of the hyper-parameters. For the second problem of blinded sample size re-estimation, we use prior predictive distributions to facilitate calculation of the interim test statistic in a blinded manner while controlling the Type I error. The determination of the test statistic in a blinded manner continues to be nettling problem for researchers. The first problem is typical of traditional experimental designs, while the second problem extends into the realm of adaptive designs. To the best of our knowledge, the approaches we suggest for both problems have never been done hitherto, and extend the current research on both topics. The advantages of our approach, as far as we see it, are unity and coherence of statistical procedures, systematic and methodical incorporation of prior knowledge, and ease of calculation and interpretation.
Temple University--Theses
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Tan, Say Beng. "Bayesian decision theoretic methods for clinical trials." Thesis, Imperial College London, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312988.

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Safaie, Nasser. "A fully Bayesian approach to sample size determination for verifying process improvement." Diss., Wichita State University, 2010. http://hdl.handle.net/10057/3656.

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There has been significant growth in the development and application of Bayesian methods in industry. The Bayes’ theorem describes the process of learning from experience and shows how knowledge about the state of nature is continually modified as new data become available. This research is an effort to introduce the Bayesian approach as an effective tool for evaluating process adjustments aimed at causing a change in a process parameter. This is usually encountered in scenarios where the process is found to be stable but operating away from the desired level. In these scenarios, a number of changes are proposed and tested as part of the improvement efforts. Typically, it is desired to evaluate the effect of these changes as soon as possible and take appropriate actions. Despite considerable research efforts to utilize the Bayesian approach, there are few guidelines for loss computation and sample size determination. This research proposed a fully Bayesian approach for determining the maximum economic number of measurements required to evaluate and verify such efforts. Mathematical models were derived and used to establish implementation boundaries from economic and technical viewpoints. In addition, numerical examples were used to illustrate the steps involved and highlight the economic advantages of the proposed procedures.
Thesis (Ph.D.)--Wichita State University, College of Engineering, Dept. of Industrial and Manufacturing Engineering
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Kaouache, Mohammed. "Bayesian modeling of continuous diagnostic test data: sample size and Polya trees." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=107833.

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Parametric models such as the bi-normal have been widely used to analyse datafrom imperfect continuous diagnostic tests. Such models rely on assumptions thatmay often be unrealistic and/or unveri_able, and in such cases nonparametric modelspresent an attractive alternative. Further, even when normality holds, researcherstend to underestimate the sample size required to accurately estimate disease preva-lence from bi-normal models when densities from diseased and non-diseased subjectsoverlap. In this thesis we investigate both of these problems. First, we study theuse of nonparametric Polya tree models to analyze continuous diagnostic test data.Since we do not assume a gold standard test is available, our model includes a latentclass component, the latent data being the unknown true disease status for each sub-ject. Second, we develop methods for the sample size determination when designingstudies with continuous diagnostic tests. Finally, we show how Bayes factors can beused to compare the _t of Polya tree models to parametric bi-normal models. Bothsimulations and a real data illustration are included.
Les modèles paramétriques tel que le modèle binormal ont été largement utilisés pour analyser les données provenant de tests de diagnostic continus et non parfaits. De tels modèles reposent sur des suppositions souvent non réalistes et/ou non verifiables, et dans de tels cas les modèles nonparamétriques représentent une alternative attrayante. De plus, même quand la supposition de normalité est rencontrée les chercheurs ont tendence à sous-estimer la taille d'échantillon requise pour estimer avec exactitude la prédominance d'une maladie à partir de ces modèles bi-normaux quand les densités associées aux sujets malades se chevauchent avec celles associées aux sujets non malades. D'abord, nous étudions l'utilisation de modèles nonparametriques d'arbres de Polya pour analyser les données provenant de tests de diagnostic continus. Puisque nous ne supposons pas l'existance d'un test étalon d'or, notre modèle contient une composante de classe latente, les données latentes étant le vrai état de maladie de chaque sujet. Ensuite nous développons des méthodes pourla determination de la taille d'échantillon quand on planifie des études avec des tests de diagnostic continus. Finalement, nous montrons comment les facteurs de Bayes peuvent être utilisés pour comparer la qualité d'ajustement de modèles d'arbres de Polya à celles de modèles paramétriques binormaux. Des simulations ansi que des données réelles sont incluses.
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Ma, Junheng. "Contributions to Numerical Formal Concept Analysis, Bayesian Predictive Inference and Sample Size Determination." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1285341426.

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Kikuchi, Takashi. "A Bayesian cost-benefit approach to sample size determination and evaluation in clinical trials." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:f5cb4e27-8d4c-4a80-b792-469e50efeea2.

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Current practice for sample size computations in clinical trials is largely based on frequentist or classical methods. These methods have the drawback of requiring a point estimate of the variance of treatment effect and are based on arbitrary settings of type I and II errors. They also do not directly address the question of achieving the best balance between the costs of the trial and the possible benefits by using a new medical treatment, and fail to consider the important fact that the number of users depends on evidence for improvement compared with the current treatment. A novel Bayesian approach, Behavioral Bayes (or BeBay for short) (Gittins and Pezeshk, 2000a,b, 2002a,b; Pezeshk, 2003), assumes that the number of patients switching to the new treatment depends on the strength of the evidence which is provided by clinical trials, and takes a value between zero and the number of potential patients in the country. The better a new treatment, the more patients switch to it and the more the resulting benefit. The model defines the optimal sample size to be the sample size that maximises the expected net benefit resulting from a clinical trial. Gittins and Pezeshk use a simple form of benefit function for paired comparisons between two medical treatments and assume that the variance of the efficacy is known. The research in this thesis generalises these original conditions by introducing a logistic benefit function to take account of differences in efficacy and safety between two drugs. The model is also extended to the more general cases of unpaired comparisons and unknown variance. The expected net benefit defined by Gittins and Pezeshk is based on the efficacy of the new drug only. It does not consider the incidence of adverse reactions and their effect on patients’ preferences. Here we include the costs of treating adverse reactions and calculate the total benefit in terms of how much the new drug can reduce societal expenditure. We describe how our model may be used for the design of phase III clinical trials, cluster randomised clinical trials and bridging studies. This is done in some detail and using illustrative examples based on published studies. For phase III trials we allow the possibility of unequal treatment group sizes, which often occur in practice. Bridging studies are those carried out to extend the range of applicability of an established drug, for example to new ethnic groups. Throughout the objective of our procedures is to optimise the costbenefit in terms of national health-care. BeBay is the leading methodology for determining sample sizes on this basis. It explicitly takes account of the roles of three decision makers, namely patients and doctors, pharmaceutical companies and the health authority.
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Books on the topic "Bayesian Sample size"

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Trappenberg, Thomas P. Fundamentals of Machine Learning. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198828044.001.0001.

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Machine learning is exploding, both in research and for industrial applications. This book aims to be a brief introduction to this area given the importance of this topic in many disciplines, from sciences to engineering, and even for its broader impact on our society. This book tries to contribute with a style that keeps a balance between brevity of explanations, the rigor of mathematical arguments, and outlining principle ideas. At the same time, this book tries to give some comprehensive overview of a variety of methods to see their relation on specialization within this area. This includes some introduction to Bayesian approaches to modeling as well as deep learning. Writing small programs to apply machine learning techniques is made easy today by the availability of high-level programming systems. This book offers examples in Python with the machine learning libraries sklearn and Keras. The first four chapters concentrate largely on the practical side of applying machine learning techniques. The book then discusses more fundamental concepts and includes their formulation in a probabilistic context. This is followed by chapters on advanced models, that of recurrent neural networks and that of reinforcement learning. The book closes with a brief discussion on the impact of machine learning and AI on our society.
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Book chapters on the topic "Bayesian Sample size"

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Chow, Shein-Chung, Jun Shao, Hansheng Wang, and Yuliya Lokhnygina. "Bayesian Sample Size Calculation." In Sample Size Calculations in Clinical Research: Third Edition, 297–320. Third edition. | Boca Raton : Taylor & Francis, 2017. | Series: Chapman & Hall/CRC biostatistics series | “A CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa plc.”: Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315183084-13.

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Yang, Harry, and Steven J. Novick. "Bayesian Estimation of Sample Size and Power." In Bayesian Analysis with R for Drug Development, 41–60. Boca Raton : CRC Press, Taylor & Francis Group, 2019.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781315100388-3.

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Tsai, Chin-Pei, and Kathryn Chaloner. "Using Prior Opinions to Examine Sample Size in Two Clinical Trials." In Case Studies in Bayesian Statistics Volume V, 407–21. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0035-9_13.

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De Santis, F., and M. Perone Pacifico. "Two Experimental Settings in Clinical Trials: Predictive Criteria for Choosing the Sample Size in Interval Estimation." In Applied Bayesian Statistical Studies in Biology and Medicine, 109–30. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4613-0217-9_7.

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Lingappaiah, G. S. "Bayes Inference in Life Tests When Samples Sizes are Fixed or Random." In Probability and Bayesian Statistics, 335–45. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-1885-9_34.

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Kooli, Imen, and Mohamed Limam. "Economically Designed Bayesian np Control Charts Using Dual Sample Sizes for Long-Run Processes." In Studies in Classification, Data Analysis, and Knowledge Organization, 219–32. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25147-5_14.

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"Bayesian Sample Size Calculation." In Chapman & Hall/CRC Biostatistics Series, 327–53. Chapman and Hall/CRC, 2007. http://dx.doi.org/10.1201/9781584889830.ch13.

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Miočević, Milica, Roy Levy, and Rens van de Schoot. "Introduction to Bayesian Statistics." In Small Sample Size Solutions, 3–12. Routledge, 2020. http://dx.doi.org/10.4324/9780429273872-2.

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Bhattacharjee, Atanu. "Sample Size Determination." In Bayesian Approaches in Oncology Using R and OpenBUGS, 13–29. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9780429329449-3.

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Kruschke, John K. "Goals, Power, and Sample Size." In Doing Bayesian Data Analysis, 359–98. Elsevier, 2015. http://dx.doi.org/10.1016/b978-0-12-405888-0.00013-1.

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Conference papers on the topic "Bayesian Sample size"

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Lee, Jaesung, Shiyu Zhou, and Junhong Chen. "Sequential Robust Parameter Design With Sample Size Selection." In ASME 2022 17th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/msec2022-85690.

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Abstract In designing engineering systems, it is crucial to find a robust design whose responses have minimal variations and satisfies a constraint on the mean, also known as robust parameter design (RPD). Such optimization is challenging because collecting data is often expensive, and modern engineering systems commonly produce responses whose mean and variance are complex unknown functions of control variables. To address these challenges, we propose a stochastic constrained Bayesian optimization for RPD. We construct the Gaussian process dual response surrogate models for the mean and variance of the response by the sample mean and sample variance. The predicted mean and quantified uncertainties through the surrogate models are utilized to exploit the predictions and explore the regions with high uncertainty in the proposed Bayesian optimization method. Because the sample size of the sample mean and sample variance affects the performance significantly, we propose an effective sample size selection scheme, which effectively balances between exploitation and exploration during optimization. The performance of our method is demonstrated in numerical and case studies. In the case study, we used the real-world data for the robust design of the Graphene field-effect transistor nanosensors.
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Dong, Guangling, Chi He, Zhenguo Dai, Yanchang Huang, and Xiaochu Hang. "Bayesian Sample Size Optimization Method for Integrated Test Design of Missile Hit Accuracy." In 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005510902440253.

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Huangpeng, Qizi, Xiaojun Duan, Yinhui Zhang, and Wenwei Huang. "Sample Size Design of Launch Vehicle based on SPOT and Bayesian Recursive Estimation." In 2022 41st Chinese Control Conference (CCC). IEEE, 2022. http://dx.doi.org/10.23919/ccc55666.2022.9901630.

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Han, Lei, Ping Jiang, Yuanliang Yu, and Bo Guo. "Bayesian reliability evaluation for customized products with zero-failure data under small sample size." In 2014 International Conference on Reliability, Maintainability and Safety (ICRMS). IEEE, 2014. http://dx.doi.org/10.1109/icrms.2014.7107334.

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Xing, Y. Y., P. Jiang, and Z. J. Cheng. "The determination method on products sample size under the condition of Bayesian sequential testing." In 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2016. http://dx.doi.org/10.1109/ieem.2016.7798154.

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Zhu, Wenbing, Zijiang Yang, Xuesong Xiao, Yuanhaowei Ji, Shuchao Li, Xue Yan, and Guoli Ji. "An Improved Bayesian Integrated ICA Approach for Control Loop Diagnosis with Small Sample Size." In 2019 International Conference on Control, Automation and Diagnosis (ICCAD). IEEE, 2019. http://dx.doi.org/10.1109/iccad46983.2019.9037932.

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Sudarsanam, Nandan, Ramya Chandran, and Daniel D. Frey. "Conducting Non-Adaptive Experiments in a Live Setting: A Bayesian Approach to Determining Optimal Sample Size." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-98335.

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Abstract This research studies the use of predetermined experimental plans in a live setting with a finite implementation horizon. In this context, we seek to determine the optimal experimental budget in different environments using a Bayesian framework. We derive theoretical results on the optimal allocation of resources to treatments with the objective of minimizing cumulative regret, a metric commonly used in online statistical learning. Our base case studies a setting with two treatments assuming Gaussian priors for the treatment means and noise distributions. We extend our study through analytical and semi-analytical techniques which explore worst-case bounds and the generalization to k treatments. We determine theoretical limits for the experimental budget across all possible scenarios. The optimal level of experimentation that is recommended by this study varies extensively and depends on the experimental environment as well as the number of available units. This highlights the importance of such an approach which incorporates these factors to determine the budget.
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Gu, Chenjie, Eli Chiprout, and Xin Li. "Efficient moment estimation with extremely small sample size via bayesian inference for analog/mixed-signal validation." In the 50th Annual Design Automation Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2463209.2488813.

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Wei, Zhigang, Fulun Yang, Dmitri Konson, and Kamran Nikbin. "A Design Approach Based on Historical Test Data and Bayesian Statistics." In ASME 2013 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/pvp2013-97627.

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Testing is still the final verification for a design even though there are substantial number of analytical and simulation methods available. Testing is seen to be also an indispensable part in the foreseeable future. Numerous test data have been generated in many testing institutions over the years and it is clear that future new tests will be conducted. Historical data with similar design and operating conditions can shed light on the current and future designs since they would share some common features when the changes are not dramatic. To effectively utilize the historical data for future design, two steps are necessary: (1) finding an approach to consistently correlate test data obtained from various conditions; (2) Use of Bayesian statistics which can provide a rational mathematical tool for extracting useful information from the historical data. In this paper, the basic Bayesian statistical procedure based on the historical data is outlined. With this information the reduction of sample size number or improving the accuracy and confidence with the same sample size are becoming possible. Examples of utilizing the historical data are also presented and the benefit of using the Bayesian statistics are highlighted.
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Wei, Zhigang, Limin Luo, Fulun Yang, and Robert Rebandt. "A Bayesian Statistics Based Design Curve Construction Method for Test Data With Extremely Small Sample Sizes." In ASME 2015 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/pvp2015-45909.

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Fatigue design curve construction is commonly used for durability and reliability assessment of engineering components subjected to cyclic loading. A wide variety of design curve construction methods have been developed over the last decades. Some of the methods have been adopted by engineering codes and widely used in industry. However, the traditional design curve construction methods usually require significant amounts of test data in order for the constructed design curves to be consistently and reliably used in product design and validation. In order to reduce the test sample size and associated testing time and cost, several Bayesian statistics based design curve construction methods have been recently successfully developed by several research groups. Among all of these methods, an efficient Monte Carlo simulation based resampling method developed by the authors of this paper is of particular importance. The method is based on a large amount of reliable historical fatigue test data, the associated probabilistic distributions of the mean and standard deviation of the failure cycles, and an advanced acceptance-rejection resampling algorithm. However, finite element analysis (FEA) methods and a special stress recovery technique are required to process the test data, which is usually a time-consuming process. A more straightforward approach that does not require these intermediate processes is strongly preferred. This study presents such an approach, in which the only historical information needed is the distribution of the standard deviation of the cycles to failure. The distribution of the mean is directly calculated from the current tested data and the Central Limit Theorem. Neither FEA nor stress recovery technique is required for this approach, and the effort put into design curve construction can be significantly reduced. This method can be used to complement the previously developed Bayesian methods.
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Reports on the topic "Bayesian Sample size"

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Cressie, Noel, and Jonathan Biele. A Sample-Size Optimal Bayesian Procedure for Sequential Pharmaceutical Trials. Fort Belvoir, VA: Defense Technical Information Center, March 1992. http://dx.doi.org/10.21236/ada248512.

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Peng, Ciyan, Jing Chen, Sini Li, and Jianhe Li. Comparative Efficacy of Chinese Herbal Injections Combined Western medicine for Non-small cell lung cancer: A Bayesian Network Meta-Analysis of randomized controlled trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2021. http://dx.doi.org/10.37766/inplasy2021.11.0068.

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Review question / Objective: Advanced lung cancer has become the top malignant tumor in terms of morbidity and mortality, and Chinese herbal injections combined with western drugs have been widely used to treat advanced non-small cell lung cancer. For this purpose, we conducted a Bayesian network analysis to systematically evaluate the efficacy of different herbal injections combined with western drugs in the treatment of NSCLC. Subjects: Patients diagnosed with NSCLC by pathological or cytological examination, locally advanced or those who refused surgical treatment were included, regardless of gender, age, stage, race, nationality and sample size; Interventions: Chinese herbal injections combined with three types of commonly used western drugs (platinum, targeted and immune agents) were used in the experimental group, while the control group was treated with western drugs alone; Study type: to report the efficacy of Chinese herbal injections combined with western drugs in the treatment of non-small cell lung cancer efficacy in a randomized controlled trial (rct) Eligible. No restrictions were imposed on language, year of publication, or publication status. Ending indicators: Main ending indicators: (1) disease control rate (DCR), DCR = (complete remission + partial remission + stable)/total number of cases. Efficacy rate = (number of improvement cases + number of stable cases)/total number of cases. (2) Secondary outcome indicators: quality of life, determined according to the KPS behavioral status scale, improvement was defined as an increase of ≥10 points in KPS score after treatment; stability was defined as an increase or decrease of <10 points in KPS score; decline was defined as a decrease of ≥10 points in KPS score. (3) The incidence of adverse reactions, including gastrointestinal reactions, white blood cell (WBC) reduction, hemoglobin (HGB) reduction, platelet (PLT) reduction, etc.
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Beverinotti, Javier, Gustavo Canavire-Bacarreza, and Alejandro Puerta. Understanding the Growth of the Middle Class in Bolivia. Inter-American Development Bank, July 2021. http://dx.doi.org/10.18235/0003407.

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In this paper we aim to disentangle how sectoral economic growth affects the growth of the middle class size using state-level data of Bolivia from 2000 to 2017, a country with limited data, breaking the three main economic activities into subsectors aiming for more specific results. By means of a Bayesian hierarchical longitudinal model for small samples, we find that the commerce and services sectors have the biggest impact, even though mining and agriculture also have a positive effect on the increase of the middle class in Bolivia. Our results also suggest that both formality and public social investment have a significant, yet smaller, effect.
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