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Articles de revues sur le sujet « Bayesian Sample size »

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Auteur : Grafiati
Publié le 10 mars 2023

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1

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

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2

Pham-Gia, T., et 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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3

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

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4

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

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5

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

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6

JOSEPH, LAWRENCE, ROXANE DU BERGER et PATRICK BÉLISLE. « BAYESIAN AND MIXED BAYESIAN/LIKELIHOOD CRITERIA FOR SAMPLE SIZE DETERMINATION ». Statistics in Medicine 16, no 7 (15 avril 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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7

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

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8

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

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

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10

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

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M'Lan, Cyr E., Lawrence Joseph et David B. Wolfson. « Bayesian sample size determination for binomial proportions ». Bayesian Analysis 3, no 2 (juin 2008) : 269–96. http://dx.doi.org/10.1214/08-ba310.

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12

Zhang, Xiao, Gary Cutter et Thomas Belin. « Bayesian sample size determination under hypothesis tests ». Contemporary Clinical Trials 32, no 3 (mai 2011) : 393–98. http://dx.doi.org/10.1016/j.cct.2010.12.012.

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13

Joseph, Lawrence, David B. Wolfson et Roxane du Berger. « Some Comments on Bayesian Sample Size Determination ». Statistician 44, no 2 (1995) : 167. http://dx.doi.org/10.2307/2348442.

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14

Nassar, M. M., S. M. Khamis et S. S. Radwan. « Geometric sample size determination in Bayesian analysis ». Journal of Applied Statistics 37, no 4 (3 mars 2010) : 567–75. http://dx.doi.org/10.1080/02664760902803248.

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15

Pham-Gia, T. « On Bayesian analysis, Bayesian decision theory and the sample size problem ». Journal of the Royal Statistical Society : Series D (The Statistician) 46, no 2 (juillet 1997) : 139–44. http://dx.doi.org/10.1111/1467-9884.00069.

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16

Sanz-Alonso, Daniel, et Zijian Wang. « Bayesian Update with Importance Sampling : Required Sample Size ». Entropy 23, no 1 (26 décembre 2020) : 22. http://dx.doi.org/10.3390/e23010022.

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Importance sampling is used to approximate Bayes’ rule in many computational approaches to Bayesian inverse problems, data assimilation and machine learning. This paper reviews and further investigates the required sample size for importance sampling in terms of the χ2-divergence between target and proposal. We illustrate through examples the roles that dimension, noise-level and other model parameters play in approximating the Bayesian update with importance sampling. Our examples also facilitate a new direct comparison of standard and optimal proposals for particle filtering.
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17

ChiChang Chang, et KuoHsiung Liao. « Bayesian Sample-size Determination for Medical Decision Making ». International Journal of Advancements in Computing Technology 5, no 8 (30 avril 2013) : 1190–97. http://dx.doi.org/10.4156/ijact.vol5.issue8.132.

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18

De Santis, Fulvio. « Using historical data for Bayesian sample size determination ». Journal of the Royal Statistical Society : Series A (Statistics in Society) 170, no 1 (janvier 2007) : 95–113. http://dx.doi.org/10.1111/j.1467-985x.2006.00438.x.

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19

Sadia, Farhana, et Syed S. Hossain. « Contrast of Bayesian and Classical Sample Size Determination ». Journal of Modern Applied Statistical Methods 13, no 2 (1 novembre 2014) : 420–31. http://dx.doi.org/10.22237/jmasm/1414815720.

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20

Brakenhoff, TB, KCB Roes et S. Nikolakopoulos. « Bayesian sample size re-estimation using power priors ». Statistical Methods in Medical Research 28, no 6 (2 mai 2018) : 1664–75. http://dx.doi.org/10.1177/0962280218772315.

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The sample size of a randomized controlled trial is typically chosen in order for frequentist operational characteristics to be retained. For normally distributed outcomes, an assumption for the variance needs to be made which is usually based on limited prior information. Especially in the case of small populations, the prior information might consist of only one small pilot study. A Bayesian approach formalizes the aggregation of prior information on the variance with newly collected data. The uncertainty surrounding prior estimates can be appropriately modelled by means of prior distributions. Furthermore, within the Bayesian paradigm, quantities such as the probability of a conclusive trial are directly calculated. However, if the postulated prior is not in accordance with the true variance, such calculations are not trustworthy. In this work we adapt previously suggested methodology to facilitate sample size re-estimation. In addition, we suggest the employment of power priors in order for operational characteristics to be controlled.
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21

M'Lan, Cyr Emile, Lawrence Joseph et David B. Wolfson. « Bayesian Sample Size Determination for Case-Control Studies ». Journal of the American Statistical Association 101, no 474 (1 juin 2006) : 760–72. http://dx.doi.org/10.1198/016214505000001023.

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22

Inoue, Lurdes Y. T., Donald A. Berry et Giovanni Parmigiani. « Relationship Between Bayesian and Frequentist Sample Size Determination ». American Statistician 59, no 1 (février 2005) : 79–87. http://dx.doi.org/10.1198/000313005x21069.

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23

Pham-Gia, T. « Sample Size Determination in Bayesian Statistics-A Commentary ». Statistician 44, no 2 (1995) : 163. http://dx.doi.org/10.2307/2348441.

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24

Brutti, Pierpaolo, Fulvio De Santis et Stefania Gubbiotti. « Robust Bayesian sample size determination in clinical trials ». Statistics in Medicine 27, no 13 (2008) : 2290–306. http://dx.doi.org/10.1002/sim.3175.

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25

Wang, Yu, Zheng Guan et Tengyuan Zhao. « Sample size determination in geotechnical site investigation considering spatial variation and correlation ». Canadian Geotechnical Journal 56, no 7 (juillet 2019) : 992–1002. http://dx.doi.org/10.1139/cgj-2018-0474.

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Site investigation is a fundamental element in geotechnical engineering practice, but only a small portion of geomaterials is sampled and tested during site investigation. This leads to a question of sample size determination: how many samples are needed to achieve a target level of accuracy for the results inferred from the samples? Sample size determination is a well-known topic in statistics and has many applications in a wide variety of areas. However, conventional statistical methods, which mainly deal with independent data, only have limited applications in geotechnical site investigation because geotechnical data are not independent, but spatially varying and correlated. Existing design codes around the world (e.g., Eurocode 7) only provide conceptual principles on sample size determination. No scientific or quantitative method is available for sample size determination in site investigation considering spatial variation and correlation of geotechnical properties. This study performs an extensive parametric study and develops a statistical chart for sample size determination with consideration of spatial variation and correlation using Bayesian compressive sensing or sampling. Real cone penetration test data and real laboratory test data are used to illustrate application of the proposed statistical chart, and the method is shown to perform well.
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26

Kuen Cheung, Ying. « Sample size formulae for the Bayesian continual reassessment method ». Clinical Trials : Journal of the Society for Clinical Trials 10, no 6 (21 août 2013) : 852–61. http://dx.doi.org/10.1177/1740774513497294.

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27

DasGupta, Anirban, et Brani Vidakovic. « Sample size problems in ANOVA Bayesian point of view ». Journal of Statistical Planning and Inference 65, no 2 (décembre 1997) : 335–47. http://dx.doi.org/10.1016/s0378-3758(97)00056-6.

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28

Joseph, Lawrence, et Patrick Bélisle. « Bayesian consensus‐based sample size criteria for binomial proportions ». Statistics in Medicine 38, no 23 (11 juillet 2019) : 4566–73. http://dx.doi.org/10.1002/sim.8316.

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29

Hand, Austin L., James D. Stamey et Dean M. Young. « Bayesian sample-size determination for two independent Poisson rates ». Computer Methods and Programs in Biomedicine 104, no 2 (novembre 2011) : 271–77. http://dx.doi.org/10.1016/j.cmpb.2010.10.010.

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30

Lan, KK Gordan, et Janet T. Wittes. « Some thoughts on sample size : A Bayesian-frequentist hybrid approach ». Clinical Trials 9, no 5 (3 août 2012) : 561–69. http://dx.doi.org/10.1177/1740774512453784.

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Background Traditional calculations of sample size do not formally incorporate uncertainty about the likely effect size. Use of a normal prior to express that uncertainty, as recently recommended, can lead to power that does not approach 1 as the sample size approaches infinity. Purpose To provide approaches for calculating sample size and power that formally incorporate uncertainty about effect size. The relevant formulas should ensure that power approaches one as sample size increases indefinitely and should be easy to calculate. Methods We examine normal, truncated normal, and gamma priors for effect size computationally and demonstrate analytically an approach to approximating the power for a truncated normal prior. We also propose a simple compromise method that requires a moderately larger sample size than the one derived from the fixed effect method. Results Use of a realistic prior distribution instead of a fixed treatment effect is likely to increase the sample size required for a Phase 3 trial. The standard fixed effect method for moving from estimates of effect size obtained in a Phase 2 trial to the sample size of a Phase 3 trial ignores the variability inherent in the estimate from Phase 2. Truncated normal priors appear to require unrealistically large sample sizes while gamma priors appear to place too much probability on large effect sizes and therefore produce unrealistically high power. Limitations The article deals with a few examples and a limited range of parameters. It does not deal explicitly with binary or time-to-failure data. Conclusions Use of the standard fixed approach to sample size calculation often yields a sample size leading to lower power than desired. Other natural parametric priors lead either to unacceptably large sample sizes or to unrealistically high power. We recommend an approach that is a compromise between assuming a fixed effect size and assigning a normal prior to the effect size.
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31

Cressie, Noel, et Jonathan Biele. « A Sample-Size-Optimal Bayesian Procedure for Sequential Pharmaceutical Trials ». Biometrics 50, no 3 (septembre 1994) : 700. http://dx.doi.org/10.2307/2532784.

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32

Islam, A. F. M. Saiful, et Lawrence I. Pettit. « Bayesian sample size determination for the bounded linex loss function ». Journal of Statistical Computation and Simulation 84, no 8 (7 janvier 2013) : 1644–53. http://dx.doi.org/10.1080/00949655.2012.757766.

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33

Sahu, S. K., et T. M. F. Smith. « A Bayesian method of sample size determination with practical applications ». Journal of the Royal Statistical Society : Series A (Statistics in Society) 169, no 2 (mars 2006) : 235–53. http://dx.doi.org/10.1111/j.1467-985x.2006.00408.x.

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34

Pezeshk, Hamid, Nader Nematollahi, Vahed Maroufy et John Gittins. « The choice of sample size : a mixed Bayesian / frequentist approach ». Statistical Methods in Medical Research 18, no 2 (29 avril 2008) : 183–94. http://dx.doi.org/10.1177/0962280208089298.

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35

Islam, A. F. M. Saiful, et L. I. Pettit. « Bayesian Sample Size Determination Using Linex Loss and Linear Cost ». Communications in Statistics - Theory and Methods 41, no 2 (15 janvier 2012) : 223–40. http://dx.doi.org/10.1080/03610926.2010.521279.

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36

Jones, P. W., et S. A. Madhi. « Bayesian minimum sample size designs for the bernoulli selection problem ». Sequential Analysis 7, no 1 (janvier 1988) : 1–10. http://dx.doi.org/10.1080/07474948808836139.

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De Santis, Fulvio. « Statistical evidence and sample size determination for Bayesian hypothesis testing ». Journal of Statistical Planning and Inference 124, no 1 (août 2004) : 121–44. http://dx.doi.org/10.1016/s0378-3758(03)00198-8.

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Brutti, Pierpaolo, Fulvio De Santis et Stefania Gubbiotti. « Bayesian-frequentist sample size determination : a game of two priors ». METRON 72, no 2 (13 mai 2014) : 133–51. http://dx.doi.org/10.1007/s40300-014-0043-2.

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Cao, Jing, J. Jack Lee et Susan Alber. « Comparison of Bayesian sample size criteria : ACC, ALC, and WOC ». Journal of Statistical Planning and Inference 139, no 12 (décembre 2009) : 4111–22. http://dx.doi.org/10.1016/j.jspi.2009.05.041.

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40

Wang, Hansheng, Shein-Chung Chow et Murphy Chen. « A Bayesian Approach on Sample Size Calculation for Comparing Means ». Journal of Biopharmaceutical Statistics 15, no 5 (1 septembre 2005) : 799–807. http://dx.doi.org/10.1081/bip-200067789.

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Stamey, James, et Richard Gerlach. « Bayesian sample size determination for case-control studies with misclassification ». Computational Statistics & ; Data Analysis 51, no 6 (mars 2007) : 2982–92. http://dx.doi.org/10.1016/j.csda.2006.01.014.

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Whitehead, John, Elsa Valdés-Márquez, Patrick Johnson et Gordon Graham. « Bayesian sample size for exploratory clinical trials incorporating historical data ». Statistics in Medicine 27, no 13 (2008) : 2307–27. http://dx.doi.org/10.1002/sim.3140.

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43

Hoofs, Huub, Rens van de Schoot, Nicole W. H. Jansen et IJmert Kant. « Evaluating Model Fit in Bayesian Confirmatory Factor Analysis With Large Samples : Simulation Study Introducing the BRMSEA ». Educational and Psychological Measurement 78, no 4 (23 mai 2017) : 537–68. http://dx.doi.org/10.1177/0013164417709314.

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Bayesian confirmatory factor analysis (CFA) offers an alternative to frequentist CFA based on, for example, maximum likelihood estimation for the assessment of reliability and validity of educational and psychological measures. For increasing sample sizes, however, the applicability of current fit statistics evaluating model fit within Bayesian CFA is limited. We propose, therefore, a Bayesian variant of the root mean square error of approximation (RMSEA), the BRMSEA. A simulation study was performed with variations in model misspecification, factor loading magnitude, number of indicators, number of factors, and sample size. This showed that the 90% posterior probability interval of the BRMSEA is valid for evaluating model fit in large samples ( N≥ 1,000), using cutoff values for the lower (<.05) and upper limit (<.08) as guideline. An empirical illustration further shows the advantage of the BRMSEA in large sample Bayesian CFA models. In conclusion, it can be stated that the BRMSEA is well suited to evaluate model fit in large sample Bayesian CFA models by taking sample size and model complexity into account.
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Xie, Xuan, Hui Feng et Bo Hu. « Bandwidth Detection of Graph Signals with a Small Sample Size ». Sensors 21, no 1 (28 décembre 2020) : 146. http://dx.doi.org/10.3390/s21010146.

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Bandwidth is the crucial knowledge to sampling, reconstruction or estimation of the graph signal (GS). However, it is typically unknown in practice. In this paper, we focus on detecting the bandwidth of bandlimited GS with a small sample size, where the number of spectral components of GS to be tested may greatly exceed the sample size. To control the significance of the result, the detection procedure is implemented by multi-stage testing. In each stage, a Bayesian score test, which introduces a prior to the spectral components, is adopted to face the high dimensional challenge. By setting different priors in each stage, we make the test more powerful against alternatives that have similar bandwidth to the null hypothesis. We prove that the Bayesian score test is locally most powerful in expectation against the alternatives following the given prior. Finally, numerical analysis shows that our method has a good performance in bandwidth detection and is robust to the noise.
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45

Al-Labadi, Luai, Yifan Cheng, Forough Fazeli-Asl, Kyuson Lim et Yanqing Weng. « A Bayesian One-Sample Test for Proportion ». Stats 5, no 4 (1 décembre 2022) : 1242–53. http://dx.doi.org/10.3390/stats5040075.

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This paper deals with a new Bayesian approach to the one-sample test for proportion. More specifically, let x=(x1,…,xn) be an independent random sample of size n from a Bernoulli distribution with an unknown parameter θ. For a fixed value θ0, the goal is to test the null hypothesis H0:θ=θ0 against all possible alternatives. The proposed approach is based on using the well-known formula of the Kullback–Leibler divergence between two binomial distributions chosen in a certain way. Then, the difference of the distance from a priori to a posteriori is compared through the relative belief ratio (a measure of evidence). Some theoretical properties of the method are developed. Examples and simulation results are included.
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Singh, Saroja Kumar, Sarat Kumar Acharya, Frederico R. B. Cruz et Roberto C. Quinino. « Bayesian sample size determination in a single-server deterministic queueing system ». Mathematics and Computers in Simulation 187 (septembre 2021) : 17–29. http://dx.doi.org/10.1016/j.matcom.2021.02.010.

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Martin, Jörg, et Clemens Elster. « GUI for Bayesian sample size planning in type A uncertainty evaluation ». Measurement Science and Technology 32, no 7 (30 avril 2021) : 075005. http://dx.doi.org/10.1088/1361-6501/abe2bd.

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Sakamaki, Kentaro, Michio Kanekiyo, Shoichi Ohwada, Kentaro Matsuura, Tomoyuki Kakizume, Fumihiro Takahashi, Akira Takazawa, Shunsuke Hagihara et Satoshi Morita. « Bayesian decision theory for clinical trials : Utility and sample size determination ». Japanese Journal of Biometrics 41, no 1 (2020) : 55–91. http://dx.doi.org/10.5691/jjb.41.55.

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49

Beavers, Daniel P., et James D. Stamey. « Bayesian sample size determination for cost-effectiveness studies with censored data ». PLOS ONE 13, no 1 (5 janvier 2018) : e0190422. http://dx.doi.org/10.1371/journal.pone.0190422.

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Wang, Yuedong. « Sample size calculations for smoothing splines based on Bayesian confidence intervals ». Statistics & ; Probability Letters 38, no 2 (juin 1998) : 161–66. http://dx.doi.org/10.1016/s0167-7152(97)00168-5.

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