Relevant bibliographies by topics / Bonferroni index

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  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'Bonferroni index'

Author: Grafiati
Published: 9 March 2023
Last updated: 11 March 2023

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Journal articles on the topic "Bonferroni index"

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Bárcena-Martin, Elena, and Jacques Silber. "The Bonferroni index and the measurement of distributional change." METRON 75, no. 1 (January 11, 2017): 1–16. http://dx.doi.org/10.1007/s40300-016-0105-8.

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Bárcena-Martin, Elena, and Jacques Silber. "On the generalization and decomposition of the Bonferroni index." Social Choice and Welfare 41, no. 4 (December 18, 2012): 763–87. http://dx.doi.org/10.1007/s00355-012-0715-x.

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TUMEN, SEMIH. "MEASURING EARNINGS INEQUALITY: AN ECONOMIC ANALYSIS OF THE BONFERRONI INDEX." Review of Income and Wealth 57, no. 4 (April 18, 2011): 727–44. http://dx.doi.org/10.1111/j.1475-4991.2011.00445.x.

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Arora, Sangeeta, Kalpana K. Mahajan, and Vikas Jangra. "A Bayesian estimation of the Gini index and the Bonferroni index for the Dagum distribution with the application of different priors." Statistics in Transition New Series 23, no. 2 (June 1, 2022): 49–68. http://dx.doi.org/10.2478/stattrans-2022-0016.

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Abstract Bayesian estimators and highest posterior density credible intervals are obtained for two popular inequality measures, viz. the Gini index and the Bonferroni index in the case of the Dagum distribution. The study considers informative and non-informative priors, i.e. the Mukherjee-Islam prior and the extension of Jeffrey’s prior, respectively, under the presumption of the Linear Exponential (LINEX) loss function. A Monte Carlo simulation study is carried out in order to obtain the relative efficiency of both the Gini and Bonferroni indices while taking into consideration different priors and loss functions. The estimated loss proves lower when using the Mukherjee-Islam prior in comparison to the extension of Jeffrey’s prior and the LINEX loss function outperforms the squared error loss function (SELF) in terms of the estimated loss. Highest posterior density credible intervals are also obtained for both these measures. The study used real-life data sets for illustration purposes.
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Silber, Jacques. "On Standard of Living and Infant Survival in Some East African Countries." ISSUE FIVE 3, no. 2 (December 28, 2019): 56–67. http://dx.doi.org/10.25079/ukhjss.v3n2y2019.pp56-67.

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To summarize the extent of infant survival in a country, three indices have been defined. The first one is the complement to 1000 of the infant mortality rate (expressed in per thousand births). The second one takes into account the inequality in infant survival rates between population subgroups. The third indicator adjusts the average infant survival rate by giving more weight to a population subgroup with a lower socio-economic status. The computation of the last two indicators requires the use of an inequality index and a concentration ratio.We used two measures of inequality, the Gini index and the Bonferroni index, as well as two concentration ratios, derived from the Gini index and related to the Bonferroni index. A short empirical illustration, based on seven East African countries, confirms the usefulness of the approach presented in this paper.
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Giorgi, G. M., and M. Crescenzi. "Bayesian estimation of the Bonferroni index from a Pareto-type I population." Statistical Methods & Applications 10, no. 1-3 (January 2001): 41–48. http://dx.doi.org/10.1007/bf02511638.

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Chakravarty, Satya R. "A deprivation-based axiomatic characterization of the absolute Bonferroni index of inequality." Journal of Economic Inequality 5, no. 3 (February 1, 2007): 339–51. http://dx.doi.org/10.1007/s10888-006-9054-4.

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Giorgi, Giovanni, and Alessio Guandalini. "Decomposing the Bonferroni Inequality Index by Subgroups: Shapley Value and Balance of Inequality." Econometrics 6, no. 2 (April 2, 2018): 18. http://dx.doi.org/10.3390/econometrics6020018.

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Renner, Gerolf. "Kritische Werte und Konfidenz- intervalle zur Interpretation von HAWIK-III IQ-Skalen, Index-Werten und Subtests." Zeitschrift für Differentielle und Diagnostische Psychologie 23, no. 3 (September 2002): 353–58. http://dx.doi.org/10.1024//0170-1789.23.3.353.

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Zusammenfassung: Um die klinische Interpretation von IQ-, Index- und Subtestwerten im HAWIK-III zu erleichtern, werden Tabellen mit kritischen Werten für paarweise und ipsative Vergleiche der IQ- und Indexwerte sowie mit 68%- und 90%-Konfidenzintervallen für die Untertests bereitgestellt. Im Gegensatz zum HAWIK-III Manual wurden die kritischen Werte für paarweise Skalenvergleiche mittels Bonferroni-Adjustierung für multiple Prüfungen berechnet.
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Du, Jiang. "Integrated Evaluation of Corporate Investment Decision Performance Based on Fuzzy Sets and BP Neural Networks." Journal of Sensors 2022 (May 28, 2022): 1–7. http://dx.doi.org/10.1155/2022/7628124.

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Enterprise investment decision performance evaluation is a complex system, which is affected by many factors. Based on the consideration of corporate strategy and stakeholders, a six-level index system is designed to evaluate the performance of corporate investment decision-making. The index-selection method combining benchmarking management and principal component analysis is established, and the enterprise management index analysis method based on neural network and fuzzy decision-making is designed. The multiattribute decision-making problem in enterprise performance evaluation is solved by using the triangular fuzzy-weighted Einstein-Bonferroni mean (TF-WEBM). The algorithm is suitable for multiattribute decision-making in a triangular fuzzy environment. Finally, the effectiveness of this method is verified by an enterprise performance evaluation example.
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Dissertations / Theses on the topic "Bonferroni index"

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VALLI, IGOR. "Scomposizione per sottopopolazioni dell'indice di Bonferroni." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2016. http://hdl.handle.net/10281/114249.

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In this thesis it is suggested a decomposition by subpopulation of the Bonferoni index. This purpose is attained at first suggesting a definition of the Bonferroni index for data expressed in terms of frequency distribution and then, using the approach proposed by Zenga (Zenga, 2012) for the decomposition of his index, it follow a decomposition by subpopulation of the Bonferroni index in two steps. The Bonferroni and Zenga indexes, as well as the Gini index (Gini, 1914), are implemented in two steps: in the first step, point measures are made, whereas in the second steps, a synthetic index is obtained averaging the point measures. Until now the proposals suggested for the decompositions of the Gini and Bonferroni indexes were based on the decomposition of the synthetic indexes; in this works, the decomposition by subpopulation is based instead on the decompositions of the point measures. Following this approach, in the first part of the suggested procedure, an additive decomposition is obtained and the contributions of each subpopulation to the point inequality measure, evaluated on the whole population, is computed. From this decomposition, other two decomposition terms are deduced: a within term, that is a measure of inequality evaluated within a subpopulation, and a between term that informs on the inequality derived from the comparison between different subpopulation. In the second part of this work, exploiting these decomposition, an additive decomposition is obtained for the synthetic index. Finally, the methodology proposed is applied on data on the net disposable households income provided by Banca d’Italia in 2014 and comparison among inequality indexes are made.
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Book chapters on the topic "Bonferroni index"

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Tarsitano, Agostino. "The Bonferroni Index Of Income Inequality." In Income and Wealth Distribution, Inequality and Poverty, 228–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-84250-4_15.

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Blanco-Mesa, Fabio, and José M. Merigó. "Bonferroni Means with the Adequacy Coefficient and the Index of Maximum and Minimum Level." In Lecture Notes in Business Information Processing, 155–66. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40506-3_16.

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Conference papers on the topic "Bonferroni index"

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Vieira, René Aloisio da Costa, Débora Sant’Anna de Andrade e. Silva, Ana Carolina Laus, Carlos Eduardo Bacchi, René Julias Costa e. Silva, Idam de Oliveira-Junior, Rui Pereira, and Rui Manuel Vieira Reis. "HOW MUCH CAN WE TRUST ON THE SELFREPORTED COLOR WHEN EVALUATING BREAST CANCER ANCESTRY." In Abstracts from the Brazilian Breast Cancer Symposium - BBCS 2021. Mastology, 2021. http://dx.doi.org/10.29289/259453942021v31s2016.

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Objective: To evaluate the association between self-reported color and ancestry in Brazilian patients with breast cancer (BC). Methods: Ethics approval 1136/2016. This was an observational, transversal, epidemiological study, evaluating 1,215 patients with BC. DNA was extracted to evaluate ancestry. For genetic ancestry, a 46 AIM-INDEL panel was used, and the polymerase chain reaction (PCR) products were subjected to capillary electrophoresis. The ancestral profile was evaluated with Structure v.2.3.3 software, for ancestry proportion, the percentages of ancestry in the different self-referred colors. For this purpose, descriptive statistics was performed (mean ± standard deviation [minimum − maximum]). To assess differences between groups, ANOVA and Bonferroni were used. Results: The color distribution was 77.9% (946) white, 17.4% (212) brown, 4.1% (50) black, 0.3% (4) yellow, and 0.2% (3) mixed. Genetically, the African ancestry proportion was significantly (p <0.001) higher in yellow (0.48 ± 0.51 [0.04–0.93]) with less difference between the other groups. Finally, the Amerindian ancestry proportion frequency was less frequent in all groups, and cafuse patients did not express differences between all race groups. Brown race group presented differences in the African and European Ancestry. Conclusion: Although we found many similarities between white color — European ancestry, black color — African ancestry, and yellow color — Asian ancestry, there is great miscegenation between patients and although they can be labeled as having one color, they do present many ancestral genes that would allow their inclusion in another race group.
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