Добірка наукової літератури з теми "Bootstrap resampling method"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Bootstrap resampling method".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Bootstrap resampling method"
Putra G, Aditio, Muhammad Arif Tiro, and Muhammad Kasim Aidid. "Metode Boostrap dan Jackknife dalam Mengestimasi Parameter Regresi Linear Ganda (Kasus: Data Kemiskinan Kota Makassar Tahun 2017)." VARIANSI: Journal of Statistics and Its application on Teaching and Research 1, no. 2 (July 12, 2019): 32. http://dx.doi.org/10.35580/variansiunm12895.
Повний текст джерелаS.W., Fransiska Grace, Sri Sulistijowati Handajani, and Titin Sri Martini. "Bootstrap Residual Ensemble Methods for Estimation of Standard Error of Parameter Logistic Regression To Hypercolesterolemia Patient Data In Health Laboratory Yogyakarta." Indonesian Journal of Applied Statistics 1, no. 1 (September 19, 2018): 29. http://dx.doi.org/10.13057/ijas.v1i1.24086.
Повний текст джерелаNaik, Bhaven, Laurence R. Rilett, Justice Appiah, and Lubinda F. Walubita. "Resampling Methods for Estimating Travel Time Uncertainty: Application of the Gap Bootstrap." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 42 (August 23, 2018): 137–47. http://dx.doi.org/10.1177/0361198118792124.
Повний текст джерелаMohd Noh, Muhamad Husnain, Mohd Akramin Mohd Romlay, Chuan Zun Liang, Mohd Shamil Shaari, and Akiyuki Takahashi. "Analysis of stress intensity factor for fatigue crack using bootstrap S-version finite element model." International Journal of Structural Integrity 11, no. 4 (March 16, 2020): 579–89. http://dx.doi.org/10.1108/ijsi-10-2019-0108.
Повний текст джерелаKashani, M., M. Arashi, and M. R. Rabiei. "Resampling in Fuzzy Regression via Jackknife-after-Bootstrap (JB)." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 29, no. 04 (August 2021): 517–35. http://dx.doi.org/10.1142/s0218488521500227.
Повний текст джерелаFitrianto, Anwar, and Punitha Linganathan. "Comparisons between Resampling Techniques in Linear Regression: A Simulation Study." CAUCHY: Jurnal Matematika Murni dan Aplikasi 7, no. 3 (October 11, 2022): 345–53. http://dx.doi.org/10.18860/ca.v7i3.14550.
Повний текст джерелаIvšinović, Josip, and Nikola Litvić. "Application of the bootstrap method on a large input data set - case study western part of the Sava Depression." Rudarsko-geološko-naftni zbornik 36, no. 5 (2021): 13–19. http://dx.doi.org/10.17794/rgn.2021.5.2.
Повний текст джерелаHung, Wen-Liang, E. Stanley Lee, and Shun-Chin Chuang. "Balanced bootstrap resampling method for neural model selection." Computers & Mathematics with Applications 62, no. 12 (December 2011): 4576–81. http://dx.doi.org/10.1016/j.camwa.2011.10.039.
Повний текст джерелаDwornicka, Renata, Andrii Goroshko, and Jacek Pietraszek. "The Smoothed Bootstrap Fine-Tuning." System Safety: Human - Technical Facility - Environment 1, no. 1 (March 1, 2019): 716–23. http://dx.doi.org/10.2478/czoto-2019-0091.
Повний текст джерелаHE, XUMING, and FEIFANG HU. "SOME RECENT ADVANCES ON BOOTSTRAP." COSMOS 01, no. 01 (May 2005): 75–86. http://dx.doi.org/10.1142/s021960770500005x.
Повний текст джерелаДисертації з теми "Bootstrap resampling method"
Yam, Chiu Yu. "Quasi-Monte Carlo methods for bootstrap." HKBU Institutional Repository, 2000. http://repository.hkbu.edu.hk/etd_ra/272.
Повний текст джерелаBAI, HAIYAN. "A NEW RESAMPLING METHOD TO IMPROVE QUALITY RESEARCH WITH SMALL SAMPLES." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1172526468.
Повний текст джерелаWillrich, Niklas. "Resampling-based tuning of ordered model selection." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2015. http://dx.doi.org/10.18452/17376.
Повний текст джерелаIn this thesis, the Smallest-Accepted method is presented as a new Lepski-type method for ordered model selection. In a first step, the method is introduced and studied in the case of estimation problems with known noise variance. The main building blocks of the method are a comparison-based acceptance criterion relying on Monte-Carlo calibration of a set of critical values and the choice of the model as the smallest (in complexity) accepted model. The method can be used on a broad range of estimation problems like function estimation, estimation of linear functionals and inverse problems. General oracle results are presented for the method in the case of probabilistic loss and for a polynomial loss function. Applications of the method to specific estimation problems are studied. In a next step, the method is extended to the case of an unknown possibly heteroscedastic noise structure. The Monte-Carlo calibration step is now replaced by a bootstrap-based calibration. A new set of critical values is introduced, which depends on the (random) observations. Theoretical properties of this bootstrap-based Smallest-Accepted method are then studied. It is shown for normal errors under typical assumptions, that the replacement of the Monte-Carlo step by bootstrapping in the Smallest-Accepted method is valid, if the underlying signal is Hölder-continuous with index s > 1/4 and log(n) (p^2/n) is small for a sample size n and a maximal model dimension p.
Yazici, Ceyda. "A Computational Approach To Nonparametric Regression: Bootstrapping Cmars Method." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613708/index.pdf.
Повний текст джерелаHuang, Yifan. "Modelling and resampling based multiple testing with applications to genetics." Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1123278702.
Повний текст джерелаTitle from first page of PDF file. Document formatted into pages; contains xii, 97 p.; also includes graphics. Includes bibliographical references (p. 94-97). Available online via OhioLINK's ETD Center
Critchfield, Brian L. "Statistical Methods For Kinetic Modeling Of Fischer Tropsch Synthesis On A Supported Iron Catalyst." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1670.pdf.
Повний текст джерелаThangavelu, Karthinathan. "Quantile estimation based on the almost sure central limit theorem." Doctoral thesis, [S.l.] : [s.n.], 2006. http://webdoc.sub.gwdg.de/diss/2006/thangavelu.
Повний текст джерелаAlbuquerque, João David Ferreira de Castro. "Classification methods applied to familial hypercholesterolemia diagnosis in pediatric age." Master's thesis, 2019. http://hdl.handle.net/10451/40378.
Повний текст джерелаIntrodução: A Hipercolesterolemia Familiar (FH) é uma doença genética do metabolismo lipídico, caracterizada por níveis elevados de colesterol proveniente das lipoproteínas de baixa densidade (LDLc). A severa dislipidemia resultante leva ao desenvolvimento precoce de aterosclerose, representando um grande factor de risco de doença cardiovascular (CVD). O diagnóstico antecipado da FH encontra-se associado com uma redução significativa do risco de CVD, fundamentando a introdução de medidas terapêuticas mais precoces e agressivas. Existem diferentes critérios clínicos disponíveis para o diagnóstico da FH, sendo que apenas através de teste genético se pode confirmar o mesmo. Os critérios de Simon Broome (SB) para o diagnóstico da FH são dos mais frequentemente utilizados em contexto clínico, e são baseados na história familiar, presença de sinais físicos, e concentração plasmática de LDLc e colesterol total (TC). Quando comparados com os resultados do diagnóstico genético contudo, os critérios de SB apresentam uma elevada taxa de falsos positivos, o que constitui um pesado fardo em termos de despesas de saúde, e limita o acesso ao estudo molecular por parte de um maior universo de potenciais casos de FH. Objectivos: O objectivo principal do presente estudo foi desenvolver métodos de classificação alternativos para o diagnóstico da FH, a partir de diferentes indicadores bioquímicos, que pudessem demonstrar melhor capacidade para rastrear esta patologia comparativamente aos critérios de SB. Dois modelos distintos foram desenvolvidos para este propósito: um modelo de regressão logística (LR) e um modelo em árvore de decisão (DT). Métodos: Concentrações séricas de TC, LDLc, colesterol associado às lipoproteínas de alta densidade (HDLc), triglicerídeos (TG), apolipoproteinas AI (apoAI) e B (apoB), e lipoproteína(a) (Lp(a)) foram determinadas, e o diagnóstico molecular foi efectuado, numa amostra de 252 participantes no estudo Português de FH, em idade pediátrica (2-17 anos). Todos os participantes possuíam os critérios clínicos de dislipidemia, e não se encontravam sob medicação hipolipidémica durante o período de avaliação. Os modelos de LR e DT foram ajustados aos dados da amostra. Para o modelo de LR, dois valores de corte distintos foram definidos, através de análise de curvas ROC (receiver operating characteristics), de acordo com os métodos do índice de Youden e mínimo valor-p (min p). A construção da DT foi baseada em medidas de redução da entropia, ou ganho de informação. Uma versão modificada da DT foi implementada, na qual se procedeu à exclusão sequencial de variáveis á medida que eram incluídas no modelo. Este processo permite produzir uma regra de classificação que utiliza valores de corte únicos para cada biomarcador, simplificando a sua interpretação. Diferentes características operacionais (OC) foram estimadas para todos os modelos: acurácia (Acc), sensibilidade (Se), especificidade (Spe), valor preditivo positivo (PPV) e valor preditivo negativo (NPV). Estas OC foram calculadas através de uma matriz de confusão, considerando os resultados do teste molecular como o verdadeiro estado da doença. O modelo de LR e a DT com melhor desempenho foram comparados com os critérios bioquímicos de SB, através de técnicas de bootstrap resampling. Os valores da média e da mediana para as OC de 200 amostras bootstrap foram utilizados para comparação da performance preditiva dos modelos. Resultados: A função logit para o modelo de LR final foi expressa como g(π) = -7:083 +0:086 X LDLc - 0:041 X TG - 0:037 X apoAI. O modelo DT com melhor desempenho incluiu as variáveis LDLc, TG, apoAI, apoB e HDLc, por ordem descendente de importância. Entre os diferentes métodos de classificação, os valores de Acc, Spe e PPV foram mais elevados para o modelo DT, seguido do modelo LR com valor de corte (c) definido pelo método min p (c = 0:35). Os valores mais reduzidos para estas OC são encontrados com os critérios de SB (p < 0:01). Valores mais elevados de Se e NPV por outro lado, são alcançados pelos critérios de SB, e pelo modelo de LR com o valor de corte calculado através do índice de Youden (c = 0:17). O modelo de LR utilizando este ponto de corte revela contudo valores significativamente mais elevados de Acc, Spe e NPV (p < 0:01) em relação aos critérios de SB. Conclusões: Tanto o modelo de LR como DT parecem ser alternativas válidas aos tradicionais critérios clínicos para diagnóstico da FH. Parece ser possível ajustar o valor de corte do modelo de LR para obter níveis de Se similares aos observados para os critérios de SB, com uma retenção de casos falsos positivos significativamente menor. A validação destes resultados por dados adicionais, indicaria indubitavelmente este método como preferível entre os dois, e poderá ter um impacto muito significativo em termos de relação custo-efectividade. Ao evitar a repetição de variáveis predictoras, e providenciar valores de corte únicos para cada biomarcador, o modelo DT modificado assume uma estrutura que se assemelha aos critérios médicos clássicos, e pode portanto ser facilmente utilizado na prática clínica. Parece que, apesar de serem baseados em metodologias distintas, tanto o modelo de LR como a DT são capazes de dividir a amostra de acordo com os indicadores bioquímicos mais relevantes para o diagnóstico da FH. De acordo com ambos os métodos de classificação, a presença de FH encontra-se directamente relacionada com os níveis de LDLc, e inversamente relacionada com as concentrações de TG e apoAI, por esta ordem de importância. O modelo de classificação preferido, assim como as especificações do mesmo, podem variar em função das OC que são consideradas mais importantes, e do contexto em que este é aplicado.
Introduction: Familial Hypercholesterolemia (FH) is an inherited disorder of lipid metabolism, characterized by increased low density lipoprotein cholesterol (LDLc) levels. The resulting severe dyslipidemia leads to the early development of atherosclerosis, representing a major risk factor for cardiovascular disease (CVD). The early diagnosis of FH is associated with a significant reduction in CVD risk, supporting the introduction of precocious and more aggressive therapeutic measures. There are different clinical criteria available for the diagnosis of FH, although only genetic testing can confirm the diagnostic. Simon Broome (SB) criteria for FH diagnosis are among the most frequently used in clinical setting, and are based on family history, presence of physical signs, and LDLc and total cholesterol (TC) levels. When compared to genetic diagnosis results however, SB criteria present a high false positive rate, which constitutes a heavy burden in terms of healthcare costs, and limits the access to the genetic study of a larger universe of potential FH cases. Aim: The main purpose of this work was to develop alternative classification methods for FH diagnosis, based on different biochemical indicators, with improved ability to screen for FH cases in comparison to SB criteria. Two different models were developed for this purpose: a logistic regression (LR), and a decision tree (DT) model. Methods: Serum concentrations of TC, LDLc, high density lipoprotein cholesterol (HDLc), triglycerides (TG), apolipoproteins AI (apoAI) and B (apoB), and lipoprotein(a) (Lp(a)) were determined, and genetic diagnosis was performed, in a sample of 252 participants in the Portuguese FH Study, at pediatric age (2-17 years). All patients met the clinical criteria for dyslipidemia, and were not under hypolipidemic medication during the evaluation period. LR and DT models were fitted to sample data. For the LR model, two different cutoff points were defined, through receiver operating characteristics (ROC) curve analysis, following Yoden index and minimum p-value (min p) methods. The DT was built based on entropy reduction, or information gain measures. A modified version of the DT method was implemented, consisting in the sequential exclusion of predictor variables as they are introduced in the model. This allows producing a classification rule that uses single cutpoints for biomarkers, simplifying its interpretation. Different operating characteristics (OC) were estimated for all models: accuracy (Acc), sensitivity (Se), specificity (Spe), positive predictive value (PPV ) and negative predictive value (NPV ). These OC were calculated by generating a confusion matrix, considering molecular study results as the true state of the disease. The best performing LR and DT models were compared with SB biochemical criteria for FH diagnosis, through bootstrap resampling techniques. Median and mean values of the OC for 200 bootstrap samples were used for predictive performance comparison. Results: The logit function for the LR final model was expressed as g(π) = -7:083 + 0:086 X LDLc -0:041 X TG - 0:037X apoAI. The best performing DT model included the variables LDLc, TG, apoAI, apoB and HDLc, by descending order of importance. Between the different classification methods, Acc, Spe and PPV were higher in the DT model, followed by the LR model with the cut point value (c) defined by the min p method (c = 0:35). The lower values in these OC are found for SB criteria (p < 0:01). Higher Se and NPV on the other hand, are achieved by SB criteria, and the LR model with the cutpoint value calculated by Youden index (c = 0:17). However, the LR model using this cutpoint achieves significantly higher Acc, Spe and NPV than SB criteria (p < 0:01). Conclusions: Both LR and DT models seem to be a valid alternative to traditional clinical criteria for FH diagnosis. It seems possible to adjust the cutoff value in the LR model for similar Se levels as the ones observed in SB criteria, with significantly less false positive retention. To be validated by additional data, this would undoubtedly indicate this method as preferable between the two, and can have a very important impact in terms of cost-effectiveness. By avoiding the repetition of predictor variables, and providing single cutoff values for each biomarker, the modified DT model assumes a structure that typically resembles medical criteria, and can therefore be easily used in clinical practice. It seems that, in spite using different methodological approaches, both LR and DT models are able to divide the sample according to the most relevant biochemical characteristics for FH diagnosis. According to both classification methods, presence of FH is directly related to LDLc levels, and inversely related to TG and ApoAI concentrations, by this order of importance. The preferred classification model, as well as model specifications, may vary as a function of the OC that are considered more important, and context in which it is applied.
Книги з теми "Bootstrap resampling method"
McMurry, Timothy, and Dimitris Politis. Resampling methods for functional data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.7.
Повний текст джерелаCheng, Russell. Non-Standard Parametric Statistical Inference. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.001.0001.
Повний текст джерелаFerraty, Frédéric, and Yves Romain, eds. The Oxford Handbook of Functional Data Analysis. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.001.0001.
Повний текст джерелаЧастини книг з теми "Bootstrap resampling method"
Lahiri, S. N. "Bootstrap Methods." In Resampling Methods for Dependent Data, 17–43. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-1-4757-3803-2_2.
Повний текст джерелаLahiri, S. N. "Model-Based Bootstrap." In Resampling Methods for Dependent Data, 199–220. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-1-4757-3803-2_8.
Повний текст джерелаLahiri, S. N. "Frequency Domain Bootstrap." In Resampling Methods for Dependent Data, 221–40. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-1-4757-3803-2_9.
Повний текст джерелаLahiri, S. N. "Comparison of Block Bootstrap Methods." In Resampling Methods for Dependent Data, 115–44. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-1-4757-3803-2_5.
Повний текст джерелаLahiri, S. N. "Properties of Block Bootstrap Methods for the Sample Mean." In Resampling Methods for Dependent Data, 45–71. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-1-4757-3803-2_3.
Повний текст джерелаDeheuvels, Paul, and Gérard Derzko. "Asymptotic Certainty Bands for Kernel Density Estimators Based upon a Bootstrap Resampling Scheme." In Statistical Models and Methods for Biomedical and Technical Systems, 171–86. Boston, MA: Birkhäuser Boston, 2008. http://dx.doi.org/10.1007/978-0-8176-4619-6_13.
Повний текст джерелаChatterjee, Arpita, and Santu Ghosh. "A Review of Bootstrap Methods in Ranked Set Sampling." In Ranked Set Sampling Models and Methods, 171–89. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-7556-7.ch008.
Повний текст джерелаRodrigues Liska, Gilberto, Luiz Alberto Beijo, Marcelo Ângelo Cirillo, Flávio Meira Borém, and Fortunato Silva de Menezes. "Intensive Computational Method Applied for Assessing Specialty Coffees by Trained and Untrained Consumers." In Confidence Regions - Applications, Tools and Challenges of Estimation [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.95234.
Повний текст джерелаEdge, M. D. "Semiparametric estimation and inference." In Statistical Thinking from Scratch, 139–64. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198827627.003.0010.
Повний текст джерелаWang, Yun, and Lee Seidman. "Risk Factors to Retrieve Anomaly Intrusion Information and Profile User Behavior." In Information Security and Ethics, 2407–21. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-937-3.ch159.
Повний текст джерелаТези доповідей конференцій з теми "Bootstrap resampling method"
Monbet, Vale´rie, Pierre Ailliot, and Marc Prevosto. "Nonlinear Simulation of Multivariate Sea State Time Series." In ASME 2005 24th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2005. http://dx.doi.org/10.1115/omae2005-67490.
Повний текст джерелаTyler, James Clay, and T. Agami Reddy. "Using the Bootstrap Method to Determine Uncertainty Bounds for Change Point Utility Bill Energy Models." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-62209.
Повний текст джерелаBoufidi, Elissavet, Sergio Lavagnoli, and Fabrizio Fontaneto. "A Probabilistic Uncertainty Estimation Method for Turbulence Parameters Measured by Hot Wire Anemometry in Short Duration Wind Tunnels." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-90461.
Повний текст джерелаRandell, David, Yanyun Wu, Philip Jonathan, and Kevin Ewans. "Modelling Covariate Effects in Extremes of Storm Severity on the Australian North West Shelf." In ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/omae2013-10187.
Повний текст джерелаLewis, John R., Dusty Brooks, and Michael L. Benson. "Methods for Uncertainty Quantification and Comparison of Weld Residual Stress Measurements and Predictions." In ASME 2017 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/pvp2017-65552.
Повний текст джерела"DEAL EFFECT CURVE AND PROMOTIONAL MODELS - Using Machine Learning and Bootstrap Resampling Test." In International Conference on Pattern Recognition Applications and Methods. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003732705370540.
Повний текст джерелаJekel, Charles F., and Vicente Romero. "Bootstrapping and Jackknife Resampling to Improve Sparse-Sample UQ Methods for Tail Probability Estimation." In ASME 2019 Verification and Validation Symposium. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/vvs2019-5127.
Повний текст джерелаSilva, Vander, Katerina Lukasova, and Maria Carthery Goulart. "APPLICATION OF A BATTERY OF EXECUTIVE FUNCTIONS IN HEALTHY ELDERLY: A PILOT STUDY." In XIII Meeting of Researchers on Alzheimer's Disease and Related Disorders. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1980-5764.rpda106.
Повний текст джерела