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Gregg, Mary, Somnath Datta, and Doug Lorenz. "Variance estimation in tests of clustered categorical data with informative cluster size." Statistical Methods in Medical Research 29, no. 11 (June 8, 2020): 3396–408. http://dx.doi.org/10.1177/0962280220928572.
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In the analysis of clustered data, inverse cluster size weighting has been shown to be resistant to the potentially biasing effects of informative cluster size, where the number of observations within a cluster is associated with the outcome variable of interest. The method of inverse cluster size reweighting has been implemented to establish clustered data analogues of common tests for independent data, but the method has yet to be extended to tests of categorical data. Many variance estimators have been implemented across established cluster-weighted tests, but potential effects of differing methods on test performance has not previously been explored. Here, we develop cluster-weighted estimators of marginal proportions that remain unbiased under informativeness, and derive analogues of three popular tests for clustered categorical data, the one-sample proportion, goodness of fit, and independence chi square tests. We construct these tests using several variance estimators and show substantial differences in the performance of cluster-weighted tests based on variance estimation technique, with variance estimators constructed under the null hypothesis maintaining size closest to nominal. We illustrate the proposed tests through an application to a data set of functional measures from patients with spinal cord injuries participating in a rehabilitation program.
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Sankaran, M., M. R. Dinesh, D. C. S. Gowda, and R. Venugopalan. "Genetic analysis in mango (Mangifera indica L.) based on fruit characteristics of 400 genotypes." Journal of Horticultural Sciences 15, no. 2 (December 31, 2020): 161–72. http://dx.doi.org/10.24154/jhs.2020.v15i02.007.
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The analysis of variance for 6 quantitative traits and 30 qualitative traits showed significant differences among the 400 genotypes of mango which indicates the existence of high heterozygosity. Among the 18 clusters formed, the highest fruit weight of 1404.27 g was recorded in cluster 10 followed by cluster 15 with 1280.67g whereas the lowest fruit weight was recorded in cluster 16 (30.94g). The highest fruit length (22.03 cm) was recorded in cluster 10 followed by 17.80 cm in cluster 14. Similarly, the fruit diameter was highest (12.18 cm) in cluster 10 followed by 12.03 cluster 4. The fruit thickness was highest (10.60 cm) in cluster 15 followed by cluster 4 with 9.96 cm. The pulp recovery was maximum (87.16%) in cluster-14 followed by clusters 4 and 18 with 79.28 and 78.41 %, respectively. Clusters 15 had the varieties meant for pickle making and possessed less TSS whereas the TSS of above 19°B was recorded in cluster 2. The maximum inter-cluster (D2) value was obtained between cluster 10 and cluster 11. These clusters may be used for hybridization programs due to wide variability and the possibility of transgressive sergeants. Estimates of phenotypic variance and genotypic variance had only a narrow difference for all six characters studied indicating that these characters are not much influenced by environmental factors and highly heritable which can be exploited by adopting clonal selection or selection of chance seedlings and selection as parents for breeding purpose.
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Sankaran, M., M. R. Dinesh, D. C. S. Gowda, and R. Venugopalan. "Genetic analysis in mango (Mangifera indica L.) based on fruit characteristics of 400 genotypes." Journal of Horticultural Sciences 15, no. 2 (December 31, 2020): 161–72. http://dx.doi.org/10.24154/jhs.v15i2.944.
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The analysis of variance for 6 quantitative traits and 30 qualitative traits showed significant differences among the 400 genotypes of mango which indicates the existence of high heterozygosity. Among the 18 clusters formed, the highest fruit weight of 1404.27 g was recorded in cluster 10 followed by cluster 15 with 1280.67g whereas the lowest fruit weight was recorded in cluster 16 (30.94g). The highest fruit length (22.03 cm) was recorded in cluster 10 followed by 17.80 cm in cluster 14. Similarly, the fruit diameter was highest (12.18 cm) in cluster 10 followed by 12.03 cluster 4. The fruit thickness was highest (10.60 cm) in cluster 15 followed by cluster 4 with 9.96 cm. The pulp recovery was maximum (87.16%) in cluster-14 followed by clusters 4 and 18 with 79.28 and 78.41 %, respectively. Clusters 15 had the varieties meant for pickle making and possessed less TSS whereas the TSS of above 19°B was recorded in cluster 2. The maximum inter-cluster (D2) value was obtained between cluster 10 and cluster 11. These clusters may be used for hybridization programs due to wide variability and the possibility of transgressive sergeants. Estimates of phenotypic variance and genotypic variance had only a narrow difference for all six characters studied indicating that these characters are not much influenced by environmental factors and highly heritable which can be exploited by adopting clonal selection or selection of chance seedlings and selection as parents for breeding purpose.
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Yasmeen, Uzma, Muhammad Noor-ul-Amin, and Muhammad Hanif. "Variance estimation in stratified adaptive cluster sampling." Statistics in Transition New Series 23, no. 1 (March 1, 2022): 173–84. http://dx.doi.org/10.2478/stattrans-2022-0010.
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Abstract In many sampling surveys, the use of auxiliary information at either the design or estimation stage, or at both these stages is usual practice. Auxiliary information is commonly used to obtain improved designs and to achieve a high level of precision in the estimation of population density. Adaptive cluster sampling (ACS) was proposed to observe rare units with the purpose of obtaining highly precise estimations of rare and specially clustered populations in terms of least variances of the estimators. This sampling design proved to be more precise than its more conventional counterparts, including simple random sampling (SRS), stratified sampling, etc. In this paper, a generalised estimator is anticipated for a finite population variance with the use of information of an auxiliary variable under stratified adaptive cluster sampling (SACS). The bias and mean square error expressions of the recommended estimators are derived up to the first degree of approximation. A simulation study showed that the proposed estimators have the least estimated mean square error under the SACS technique in comparison to variance estimators in stratified sampling.
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Veenman, C. J., M. J. T. Reinders, and E. Backer. "A maximum variance cluster algorithm." IEEE Transactions on Pattern Analysis and Machine Intelligence 24, no. 9 (September 2002): 1273–80. http://dx.doi.org/10.1109/tpami.2002.1033218.
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A.K, et al., Shukla. "Variance Function in Cluster Sampling." International Journal of Computational and Theoretical Statistics 2, no. 1 (May 1, 2015): 25–30. http://dx.doi.org/10.12785/ijcts/020103.
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Terry, L. Irene, and Gloria DeGrandi-Hoffman. "MONITORING WESTERN FLOWER THRIPS (THYSANOPTERA: THRIPIDAE) IN “GRANNY SMITH” APPLE BLOSSOM CLUSTERS." Canadian Entomologist 120, no. 11 (November 1988): 1003–16. http://dx.doi.org/10.4039/ent1201003-11.
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AbstractThe efficiency and accuracy of sampling western flower thrips (Frankliniella occidentalis [Pergande]) from “Granny Smith” apple blossom clusters were analyzed during 1986–1987 to develop a sampling plan for research purposes. The accuracy of the “shake” method was compared with an “extraction” process of each of three blossom cluster types: pink, open, and petalless (petal fall). Thrip extractions from combined clusters revealed that a 9-s and 6-s “shake” removed 84 and 74%, of the thrips, respectively, but a 3-s “shake” removed 53%, and was more variable. Open blossom clusters always had higher thrips densities than either pink or petal fall clusters, regardless of the bloom state. The effects of cardinal position within trees were not consistent over time. Clusters from the top of the canopy had more thrips than lower canopy clusters, and apical clusters had more thrips than basal clusters during peak bloom. Variance component analyses indicated that thrips counts from clusters within tree were more variable than counts among trees, even when cluster types were analyzed separately. Two sets of indices (Iwao’s regression of mean crowding on mean density and Taylor’s regression of log variance on log mean density) for each cluster type indicated aggregated spatial patterns. Precision level sampling plans were developed using Iwao’s regression coefficients.
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Vostrá Vydrová, Hana, and Zuzana Novotná. "Evaluation of disparities in living standards of regions of the Czech Republic." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 60, no. 4 (2012): 407–14. http://dx.doi.org/10.11118/actaun201260040407.
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This paper focuses on regional differences between the regions of the Czech Republic. We will focus on observation of inequalities between indicators of living in different regions of the Czech Republic. The indicators are evaluated at NUTS 3 (regions), using multivariate statistical techniques - factor analysis and cluster analysis. We have identified the twelve indicators of living standards. Base data was reduced using factor analysis on the three emerging factors: 1) basic characteristics, 2) risk groups, 3) environmental variable. Cluster analysis was compiled groups of regions with similar characteristics. Cluster analysis of the breakdown of the county into three clusters based on selected indicators of living standards. They can be described as a group with higher average and lower standard of living. In the first cluster are only two regions (Liberec Region and Karlovy Vary), the third cluster is composed of Prague and the second cluster includes all other regions of the Czech Republic. To verify the evidence of differences between clusters were calculated by multivariate analysis of variance for the various indicators of living standards. An analysis of variance indicates that significant differences between clusters are caused by the standard of living indicators: GDP (regional), the average wage of women, medical equipment, culture entertainment and recreation, higher education, the disabled handicapped and older people. The data were processed in the program STATISTICA 10th.
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Scott, JoAnna M., Allan deCamp, Michal Juraska, Michael P. Fay, and Peter B. Gilbert. "Finite-sample corrected generalized estimating equation of population average treatment effects in stepped wedge cluster randomized trials." Statistical Methods in Medical Research 26, no. 2 (September 29, 2014): 583–97. http://dx.doi.org/10.1177/0962280214552092.
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Stepped wedge designs are increasingly commonplace and advantageous for cluster randomized trials when it is both unethical to assign placebo, and it is logistically difficult to allocate an intervention simultaneously to many clusters. We study marginal mean models fit with generalized estimating equations for assessing treatment effectiveness in stepped wedge cluster randomized trials. This approach has advantages over the more commonly used mixed models that (1) the population-average parameters have an important interpretation for public health applications and (2) they avoid untestable assumptions on latent variable distributions and avoid parametric assumptions about error distributions, therefore, providing more robust evidence on treatment effects. However, cluster randomized trials typically have a small number of clusters, rendering the standard generalized estimating equation sandwich variance estimator biased and highly variable and hence yielding incorrect inferences. We study the usual asymptotic generalized estimating equation inferences (i.e., using sandwich variance estimators and asymptotic normality) and four small-sample corrections to generalized estimating equation for stepped wedge cluster randomized trials and for parallel cluster randomized trials as a comparison. We show by simulation that the small-sample corrections provide improvement, with one correction appearing to provide at least nominal coverage even with only 10 clusters per group. These results demonstrate the viability of the marginal mean approach for both stepped wedge and parallel cluster randomized trials. We also study the comparative performance of the corrected methods for stepped wedge and parallel designs, and describe how the methods can accommodate interval censoring of individual failure times and incorporate semiparametric efficient estimators.
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Grilli, Leonardo, and Carla Rampichini. "The Role of Sample Cluster Means in Multilevel Models." Methodology 7, no. 4 (August 1, 2011): 121–33. http://dx.doi.org/10.1027/1614-2241/a000030.
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The paper explores some issues related to endogeneity in multilevel models, focusing on the case where the random effects are correlated with a level 1 covariate in a linear random intercept model. We consider two basic specifications, without and with the sample cluster mean. It is generally acknowledged that the omission of the cluster mean may cause omitted-variable bias. However, it is often neglected that the inclusion of the sample cluster mean in place of the population cluster mean entails a measurement error that yields biased estimators for both the slopes and the variance components. In particular, the contextual effect is attenuated, while the level 2 variance is inflated. We derive explicit formulae for measurement error biases that allow us to implement simple post-estimation corrections based on the reliability of the covariate. In the first part of the paper, the issue is tackled in a standard framework where the population cluster mean is treated as a latent variable. Later we consider a different framework arising when sampling from clusters of finite size, where the latent variable methods may have a poor performance, and we show how to effectively modify the measurement error correction. The theoretical analysis is supplemented with a simulation study and a discussion of the implications for effectiveness evaluation.
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DANIEL, W. BRENT, ROBERT E. ECKE, G. SUBRAMANIAN, and DONALD L. KOCH. "Clusters of sedimenting high-Reynolds-number particles." Journal of Fluid Mechanics 625 (April 14, 2009): 371–85. http://dx.doi.org/10.1017/s002211200900620x.
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We report experiments wherein groups of particles were allowed to sediment in an otherwise quiescent fluid contained in a large tank. The Reynolds number of the particles, defined as Re = aU/ν, ranged from 93 to 425; here, a is the radius of the spherical particle, U its settling velocity and ν the kinematic viscosity of the fluid. The characteristic size of a cluster, in a plane transverse to gravity, was measured by a ‘cluster variance’(〈r2t〉); the latter is defined as the mean square of the transverse coordinates of all constituent particles, averaged over a series of runs. The cluster variance, when plotted as a function of time, exhibited two regimes. There was a quadratic growth in the variance at short times(〈r2t〉 ∝ t2), while for long times, the cluster variance exhibited a slower sublinear growth with 〈r2t〉 ∝ t0.67. A theory, based on isotropic repulsive hydrodynamic interactions between particles, predicts the cluster variance to grow as t2/3 in the limit of long times. The theoretical framework was originally proposed to describe the long-time self-similar evolution of dilute clusters in the limit Re ≪ 1 Subramanian & Koch (J. Fluid Mech., vol. 603, 2008, p. 63), when the probability of wake-mediated interactions between particles remains asymptotically small; the latter requirement is satisfied for homogeneous spherical clusters larger than a critical radius, and is evidently satisfied for planar clusters oriented transversely to gravity. The isotropy of the interactions therefore stems from the isotropy, at large distances, of the disturbance velocity field produced by a single sedimenting particle outside its wake(which contains the compensating inflow to satisfy mass conservation). Herein, the theory is extended to large Re using an empirical correlation for the drag on a sedimenting particle. This allows one to predict, as a function of Re, the numerical prefactors in the expressions for the cluster variance of both spherical and planar clusters; the predictions for the growth exponent remain unchanged. The agreement between the theoretical and experimental growth exponents supports the hypothesis of a self-similar expansion at long times. The prefactor determined from the experimental observations is found to lie between the theoretical predictions for planar and spherical clusters.
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Hu, Wayne, and Andrey V. Kravtsov. "Sample Variance Considerations for Cluster Surveys." Astrophysical Journal 584, no. 2 (February 20, 2003): 702–15. http://dx.doi.org/10.1086/345846.
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Yasmeen, Uzma, and Mary Thompson. "Variance estimation in adaptive cluster sampling." Communications in Statistics - Theory and Methods 49, no. 10 (March 28, 2019): 2485–97. http://dx.doi.org/10.1080/03610926.2019.1576890.
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TIGGEMANN, DANIEL. "FLUCTUATIONS OF CLUSTER NUMBERS IN PERCOLATION." International Journal of Modern Physics C 13, no. 06 (July 2002): 777–81. http://dx.doi.org/10.1142/s0129183102003504.
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In order to study fluctuations in percolating systems, lattices for sizes up to L = 100 000 have been simulated several thousand times using the Hoshen–Kopelman algorithm. Distributions of cluster numbers are Gaussians for small clusters and half-sided quasi-Gaussians for large clusters. The variance of cluster numbers is proportional to the mean, with power-law deviations for small clusters. Higher moments like skewness and kurtosis were also studied.
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Pereira, Katily Luize Garcia, Daniela Aparecida de Castro Nizio, Paulo Cesar Nogueira de Lima, Roberta Pereira Miranda Fernandes, Maria de Fatima Arrigoni-Blank, Jose Carlos Freitas de Sá Filho, Luis Fernando de Andrade Nascimento, Vinicius Trindade de Souza, Kleiton Paulo Silva, and Arie Fitzgerald Blank. "Seasonal variance in the chemical composition of essential oils from Lantana camaraaccessions and their trypanocidal activity on Phytomonas serpens." Boletin Latinoamericano y del Caribe de Plantas Medicinales y Aromaticas 21, no. 6 (November 30, 2022): 737–56. http://dx.doi.org/10.37360/blacpma.22.21.6.45.
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The objective of this study was to investigate the seasonal variance of the content and chemical composition of the essential oil from Lantana camaraaccessions at two harvest times, and to analyze the trypanocidal activity on Phytomonas serpens. Essential oil content ranged from 0.13 to 0.29% in the rainy season and from 0.13 to 0.33% in the dryseason. The compounds E-caryophyllene, α-humulene, α-curcumene and germacrene D defined the formation of four chemical clusters in the rainy and dry seasons, classified as: Cluster 1 (E-caryophyllene + germacrene D); Cluster 2 (germacrene D + E-caryophyllene); Cluster 3 (α-humulene + E-caryophyllene); and Cluster 4 (α-curcumene + E-caryophyllene). All L. camaraessential oils, representing the four chemical clusters, inhibited P. serpenswith low concentrations, considering the following IC50values: 18.34±6.60 μg/mL (LAC-018, Cluster 1); 9.14±3.87 μg/mL (LAC-027, Cluster 2); 14.56±3.40 μg/mL (LAC-037, Cluster 3); and 14.97±2.68 μg/mL (LAC-019, Cluster 4).
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Niccodemi, Gianmaria, and Tom Wansbeek. "A New Estimator for Standard Errors with Few Unbalanced Clusters." Econometrics 10, no. 1 (January 21, 2022): 6. http://dx.doi.org/10.3390/econometrics10010006.
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In linear regression analysis, the estimator of the variance of the estimator of the regression coefficients should take into account the clustered nature of the data, if present, since using the standard textbook formula will in that case lead to a severe downward bias in the standard errors. This idea of a cluster-robust variance estimator (CRVE) generalizes to clusters the classical heteroskedasticity-robust estimator. Its justification is asymptotic in the number of clusters. Although an improvement, a considerable bias could remain when the number of clusters is low, the more so when regressors are correlated within cluster. In order to address these issues, two improved methods were proposed; one method, which we call CR2VE, was based on biased reduced linearization, while the other, CR3VE, can be seen as a jackknife estimator. The latter is unbiased under very strict conditions, in particular equal cluster size. To relax this condition, we introduce in this paper CR3VE-λ, a generalization of CR3VE where the cluster size is allowed to vary freely between clusters. We illustrate the performance of CR3VE-λ through simulations and we show that, especially when cluster sizes vary widely, it can outperform the other commonly used estimators.
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Noor-Ul-Amin, Muhammad, Uzma Yasmeen, and Muhammad Hanif. "Generalized variance estimators in adaptive cluster sampling using single auxiliary variable." Journal of Statistics and Management Systems 21, no. 3 (April 18, 2018): 401–15. http://dx.doi.org/10.1080/09720510.2017.1413045.
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Li, Zhen Dong, and Fei Li. "A Clustering Algorithm Based on Variance-Similarity." Applied Mechanics and Materials 333-335 (July 2013): 1306–9. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1306.
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Clustering algorithms, like K-means Algorithm, use distances in attribute space to cluster data. However the computation of distances in attribute space influences the accuracy. So innovatively, Variance-Similarity clustering algorithm defines similarity as a function of the attribute variance, and clusters data by the comparison of similarities. In computer simulation, the comparison of Variance-Similarity Algorithm and K-means Algorithm on UCI data sets presents that Variance-Similarity Algorithm has a better clustering accuracy than K-means Algorithm.
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Talekar, S. C., M. Vani Praveena, and R. G. Satish. "Genetic diversity using principal component analysis and hierarchical cluster analysis in rice." INTERNATIONAL JOURNAL OF PLANT SCIENCES 17, no. 2 (July 15, 2022): 191–96. http://dx.doi.org/10.15740/has/ijps/17.2/191-196.
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A set of 100 germplasm lines with four checks viz., BPT-5204, PSB-68, Siri1253 and MGD-101 were evaluated in augmented block design during Kharif 2020. The observations were documented for 5 quantitative traits viz., days to 50% flowering, panicle length, number of panicles per square meter, 1000 grain weight and grain yield by principal component analysis and cluster analysis to determine the relationship and genetic divergence among the individuals. The cumulative variance of 55.60% was explained by 1st two principal components (PC1 and PC2) with eigen values greater than 1. Component 1 with variance of 32.10% had contribution from days to 50% flowering, panicle length, panicles per square meter and grain yield while principal component 2 accounting 23.50% total variability has contribution from days to 50% flowering and panicles per square meter. The remaining variability of 17.68%, 16.10% and 10.60% was consolidated in PC3, PC4 and PC5. Results from cluster analysis grouped 100 germplasm lines into four clusters with minimum individuals constituted in cluster 1 and maximum individuals were found in cluster 4. The lines in cluster 1 (2.62) showed maximum divergence followed by cluster 3 (2.23). The maximum inter cluster Euclidean distance was observed between clusters 2 and cluster 3 followed by cluster 1 and cluster 2 giving a scope for selection of parents for hybridization programme from these clusters to realize high genetic variation and novel combinations for yield increment.
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Qureshi, Muhammad Nouman, Sadia Khalil, Chang-Tai Chao, and Muhammad Hanif. "Estimation of rare and clustered population variance in adaptive cluster sampling." Communications in Statistics - Theory and Methods 48, no. 21 (November 17, 2018): 5387–400. http://dx.doi.org/10.1080/03610926.2018.1513144.
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Kel’manov, A. V., A. V. Pyatkin, and V. I. Khandeev. "On the complexity of some partition problems of a finite set of points in Euclidean space into balanced clusters." Доклады Академии наук 488, no. 1 (September 24, 2019): 16–20. http://dx.doi.org/10.31857/s0869-5652488116-20.
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We consider some problems of partitioning a finite set of N points in d-dimension Euclidean space into two clusters balancing the value of (1) the quadratic variance normalized by a cluster size, (2) the quadratic variance, and (3) the size-weighted quadratic variance. We have proved the NP-completeness of all these problems.
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Kılıçman, Adem, and Jaisree Sivalingam. "Portfolio Optimization of Equity Mutual Funds—Malaysian Case Study." Advances in Fuzzy Systems 2010 (2010): 1–7. http://dx.doi.org/10.1155/2010/879453.
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We focus on the equity mutual funds offered by three Malaysian banks, namely Public Bank Berhad, CIMB, and Malayan Banking Berhad. The equity mutual funds or equity trust is grouped into four clusters based on their characteristics and categorized as inferior, stable, good performing, and aggressive funds based on their return rates, variance and treynor index. Based on the cluster analysis, the return rates and variance of clusters are represented as triangular fuzzy numbers in order to reflect the uncertainty of financial market. To find the optimal asset allocation in each cluster we develop a hybrid model of optimization and fuzzy based on return rates, variance. This was done by maximizing the fuzzy return for a tolerable fuzzy risk and minimizing the fuzzy risk for a desirable fuzzy return separately at different confidence levels.
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Aronow, Peter M., Cyrus Samii, and Valentina A. Assenova. "Cluster–Robust Variance Estimation for Dyadic Data." Political Analysis 23, no. 4 (2015): 564–77. http://dx.doi.org/10.1093/pan/mpv018.
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Dyadic data are common in the social sciences, although inference for such settings involves accounting for a complex clustering structure. Many analyses in the social sciences fail to account for the fact that multiple dyads share a member, and that errors are thus likely correlated across these dyads. We propose a non-parametric, sandwich-type robust variance estimator for linear regression to account for such clustering in dyadic data. We enumerate conditions for estimator consistency. We also extend our results to repeated and weighted observations, including directed dyads and longitudinal data, and provide an implementation for generalized linear models such as logistic regression. We examine empirical performance with simulations and an application to interstate disputes.
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Haydar, FMA, NK Paul, and MA Khaleque. "D2 statistical analysis of yield contributing traits in maize (Zea mays L.) inbreds." Bangladesh Journal of Botany 44, no. 4 (October 21, 2018): 629–34. http://dx.doi.org/10.3329/bjb.v44i4.38634.
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Investigation was carried out to determine the genetic divergence in the 25 maize inbred lines. Analysis of variance revealed highly significant differences among all the inbreds. Inbreds were grouped into five clusters, indicating the presence of genetic diversity. The clusters I, IV and V had the highest number of inbreds (6). The maximum inter-cluster distance was observed between clusters I and III (19.279) and the highest intra-cluster distance was recorded in cluster III (0.243) and also wide range of variation was observed in cluster mean performance for the characters studied. Intercrossing among the inbreds belonging to clusters II and III was suggested to develop high yielding inbreds with desirable characters.
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Zeng, Qingdong, Wenzheng Liu, and Jun Yao. "Optimization of Non-Uniform Perforation Parameters for Multi-Cluster Fracturing." Energies 15, no. 14 (July 13, 2022): 5099. http://dx.doi.org/10.3390/en15145099.
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Stress shadowing affects the simultaneous propagation of fractures from multiple perforation clusters. Employing uniform perforation parameters for all clusters cause the unbalanced growth of fractures, which arouses the demand of optimizing non-uniform perforation parameters. An optimization workflow combining a fracture propagation model and the particle swarm optimization method (PSO) is proposed for multi-cluster fracturing in this study. The fracture model considers the coupling of rock deformation and fluid flow along the wellbore and fractures, and it is solved by using the Newton iteration method. The optimization is performed by taking the variance of multiple fracture lengths as fitness value function in the frame of the PSO method. Numerical results show that using the same spacings and perforation parameters for all clusters is detrimental to the balanced growth of multiple fractures. The variance of fracture lengths drops greatly through optimization of cluster spacings and perforation number/diameter. Properly increasing the spacing and perforation number/diameter for the middle clusters promotes the balanced growth of multiple fractures. This study provides an efficient optimization workflow for multi-cluster fracturing treatment in horizontal wells.
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Ferragamo, A., J. A. Rubiño-Martín, J. Betancort-Rijo, E. Munari, B. Sartoris, and R. Barrena. "Biases in galaxy cluster velocity dispersion and mass estimates in the small Ngal regime." Astronomy & Astrophysics 641 (September 2020): A41. http://dx.doi.org/10.1051/0004-6361/201834837.
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Aims. We present a study of the statistical properties of three velocity dispersion and mass estimators: biweight, gapper, and standard deviation for a small number of galaxies (Ngal ≤ 75). Methods. Using a set of 73 numerically simulated galaxy clusters, we first characterised the statistical bias and the variance for each one of the three estimators (biweight, gapper, and standard deviation) in the determination of the velocity dispersion and the dynamical mass of the clusters through the σ–M relation. These results were used to define a new set of unbiased estimators that are able to correct for these statistical biases with a minimum increase in associated variance. We also used the same set of numerical simulations to characterise two other physical biases that affect the estimates: the effect of velocity segregation on the selection of cluster members, and the effect of using cluster members within different physical radii from the cluster centre. Results. The standard deviation (and its unbiased counterpart) is the estimator with the lowest variance estimator after the biweight and gapper. The effect of velocity segregation in the selection of galaxies within the sub-sample of the most massive galaxies in the cluster introduces a bias of 2% in the velocity dispersion estimate when it is calculated using a quarter of the most massive cluster members. We also find a dependence of the velocity dispersion estimate on the aperture radius as a fraction of R200. This is consistent with previous results in the literature. Conclusions. The proposed set of unbiased estimators effectively provides a correction of the velocity dispersion and mass estimates from the statistical and physical effects discussed above for small numbers of cluster members. When these new estimators are applied to a subset of simulated observations, they can retrieve bias-corrected values for the mean velocity dispersion and the mean mass; the standard deviation has the lowest variance. Although for a single galaxy cluster the statistical and physical effects discussed here are comparable to or slightly smaller than the bias introduced by interlopers, they are relevant when ensemble properties and scaling relations for large number of clusters are studied.
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Kennedy-Shaffer, Lee, and Michael D. Hughes. "Power and sample size calculations for cluster randomized trials with binary outcomes when intracluster correlation coefficients vary by treatment arm." Clinical Trials 19, no. 1 (December 8, 2021): 42–51. http://dx.doi.org/10.1177/17407745211059845.
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Background/Aims Generalized estimating equations are commonly used to fit logistic regression models to clustered binary data from cluster randomized trials. A commonly used correlation structure assumes that the intracluster correlation coefficient does not vary by treatment arm or other covariates, but the consequences of this assumption are understudied. We aim to evaluate the effect of allowing variation of the intracluster correlation coefficient by treatment or other covariates on the efficiency of analysis and show how to account for such variation in sample size calculations. Methods We develop formulae for the asymptotic variance of the estimated difference in outcome between treatment arms obtained when the true exchangeable correlation structure depends on the treatment arm and the working correlation structure used in the generalized estimating equations analysis is: (i) correctly specified, (ii) independent, or (iii) exchangeable with no dependence on treatment arm. These formulae require a known distribution of cluster sizes; we also develop simplifications for the case when cluster sizes do not vary and approximations that can be used when the first two moments of the cluster size distribution are known. We then extend the results to settings with adjustment for a second binary cluster-level covariate. We provide formulae to calculate the required sample size for cluster randomized trials using these variances. Results We show that the asymptotic variance of the estimated difference in outcome between treatment arms using these three working correlation structures is the same if all clusters have the same size, and this asymptotic variance is approximately the same when intracluster correlation coefficient values are small. We illustrate these results using data from a recent cluster randomized trial for infectious disease prevention in which the clusters are groups of households and modest in size (mean 9.6 individuals), with intracluster correlation coefficient values of 0.078 in the control arm and 0.057 in an intervention arm. In this application, we found a negligible difference between the variances calculated using structures (i) and (iii) and only a small increase (typically [Formula: see text]) for the independent correlation structure (ii), and hence minimal effect on power or sample size requirements. The impact may be larger in other applications if there is greater variation in the ICC between treatment arms or with an additional covariate. Conclusion The common approach of fitting generalized estimating equations with an exchangeable working correlation structure with a common intracluster correlation coefficient across arms likely does not substantially reduce the power or efficiency of the analysis in the setting of a large number of small or modest-sized clusters, even if the intracluster correlation coefficient varies by treatment arm. Our formulae, however, allow formal evaluation of this and may identify situations in which variation in intracluster correlation coefficient by treatment arm or another binary covariate may have a more substantial impact on power and hence sample size requirements.
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Asrriningtias, Salnan Ratih. "Cluster Validity Index to Determine the Optimal Number Clusters of Fuzzy Clustering for Classify Customer Buying Behavior." Journal of Development Research 5, no. 1 (May 31, 2021): 7–12. http://dx.doi.org/10.28926/jdr.v5i1.134.
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One of the strategies in order to compete in Batik MSMEs is to look at the characteristics of the customer. To make it easier to see the characteristics of customer buying behavior, it is necessary to classify customers based on similarity of characteristics using fuzzy clustering. One of the parameters that must be determined at the beginning of the fuzzy clustering method is the number of clusters. Increasing the number of clusters does not guarantee the best performance, but the right number of clusters greatly affects the performance of fuzzy clustering. So to get optimal number cluster, we can measured the result of clustering in each number cluster using the cluster validity index. From several types of cluster validity index, NPC give the best value. Optimal number cluster that obtained by the validity index is 2 and this number cluster give classify result with small variance value
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Moerbeek, Mirjam. "Sample Size Issues for Cluster Randomized Trials With Discrete-Time Survival Endpoints." Methodology 8, no. 4 (January 1, 2012): 146–58. http://dx.doi.org/10.1027/1614-2241/a000047.
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With cluster randomized trials complete groups of subjects are randomized to treatment conditions. An important question might be whether and when the subjects experience a particular event, such as smoking initiation or recovery from disease. In the social sciences the timing of such events is often measured in discrete time by using time intervals. At the planning phase of a cluster randomized trial one should decide on the number of clusters and cluster size such that parameters are estimated accurately and sufficient power on the test on treatment effect is achieved. On basis of a simulation study it is concluded that regression coefficients are estimated more accurately than the variance of the random cluster effect. In addition, it is shown that power increases with cluster size and number of clusters, and that a sufficient power cannot always be achieved by using larger cluster sizes at a fixed number of clusters.
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Hwang, Su Jeong, Ah Jeong Hong, Ji Woong Hong, and Marie Volpe. "A cluster analysis of the relationship between employee engagement and the cognition and motivation of workers." Korean Human Resource Development Strategy Institute 17, no. 4 (December 30, 2022): 155–85. http://dx.doi.org/10.21329/khrd.2022.17.4.155.
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In this study, clusters were identified according to individual cognitive characteristics (occupational future time perspective) and motivational factors (goal orientation) for employees to accept changes and increase their adaptability. In addition, the study checked for differences in employee engagement by cluster. The study sample consisted of 514 employees working for Korean companies. The research problem was verified through cluster analysis and analysis of variance. Three groups were clustered; cluster 1 ('cognitive lack performance type'), cluster 2 ('time-aware learning type'), and cluster 3 ('opportunity- aware performance type'). Employee engagement differed between the clusters, with cluster 2 showing the highest level of employee engagement, followed by cluster 3 and cluster 1. Existing studies have focused on the direct relationship between individual cognitive characteristics and motivational factors underlying employee participation. The present study adds value in that it combines two characteristics to distinguish and verify employee types.
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Goswami, B., Rb Dubey, Dalip, and Jr Choudhary. "Genetic Diversity Analysis for Seed Yield and Its Component Characters in Urdbean (Vigna Mou (L.) Hper)." Bangladesh Journal of Botany 51, no. 2 (June 28, 2022): 223–28. http://dx.doi.org/10.3329/bjb.v51i2.60418.
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Forty-six genotypes were subjected to genetic diversity studies for seed yield and its component characters using Mahalanobis’s D2 statistic. The analysis of variance indicated significant differences among 46 genotypes for all the 14 characters. Forty-six genotypes were grouped into nine clusters, out of which cluster-II had the maximum number of genotypes (16), followed by cluster-III (10), cluster-I (9) and cluster- IV (6). The rest of the clusters i.e. cluster-V, VI, VII, VIII and IX, each possessed single genotype. The intercluster distances surpassed the intra-cluster distances, expressing existence of stupendous diversity among the entries. The cluster-III and IV have the greatest diversity among their genotypic groups consequently these genotypes can potentially be utilized in varietal development programmes. Highest inter-cluster distance was noted between cluster-IV and VII followed by cluster-VII and VIII, and cluster-VI and VII, indicating ample of diversity available among them, Therefore, the genotypes of these clusters can be used as parents for crossing in hybridization programme to obtain desirable and excellent segregants. Protein content was the greatest contributor towards genetic divergence followed by number of clusters per plant and number of pods per cluster, suggesting direct selection of these characters. Bangladesh J. Bot. 51(2): 223-228, 2022 (June)
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SANWAL, S. K., B. SINGH, VIKRANT SINGH, and ANITA MANN. "Multivariate analysis and its implication in breeding of desired plant type in garden pea (Pisum sativum)." Indian Journal of Agricultural Sciences 85, no. 10 (October 5, 2015): 1298–302. http://dx.doi.org/10.56093/ijas.v85i10.52263.
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Genetic variance was evaluated in one hundred sixty genotypes of garden pea (Pisum sativum L.) for nine morphological traits through multivariate analysis. Analysis of variance indicated that the genotypes varied significantly among themselves in respect of 9 characters studied. The genotypes were grouped into 14 clusters depending upon their morphological similarity through principal component analysis. Clustering pattern indicated that majority of genotypes, i.e. 113 (70%) were genetically close to each other and grouped in 3 clusters, while apparent diversity was mainly noticed due to 47 genotypes (30%) distributed over 11 clusters. The maximum inter-cluster distance was noticed between III and XIV (61.49) followed by III and VII (51.33) and III and XII (53.27). Considering cluster mean, the genotypes of cluster III might be selected as a suitable parent for future hybridization programme. The contribution of each character towards the expression of genetic divergence indicated that 10-pod weight contributed maximum (58.29) followed by days to 50% flowering (23.83), plant height (11.31) and shelling percent (4.95%). These four characters contributed more than 98% to the total genetic divergence in the genotypes studied.
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Lin, Feng-Min, and Chang-Tai Chao. "Variances and variance estimators of the improved ratio estimators under adaptive cluster sampling." Environmental and Ecological Statistics 21, no. 2 (June 8, 2013): 285–311. http://dx.doi.org/10.1007/s10651-013-0255-2.
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Valliant, Richard, Jill A. Dever, and Frauke Kreuter. "Effects of Cluster Sizes on Variance Components in Two-Stage Sampling." Journal of Official Statistics 31, no. 4 (December 1, 2015): 763–82. http://dx.doi.org/10.1515/jos-2015-0044.
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Abstract Determining sample sizes in multistage samples requires variance components for each stage of selection. The relative sizes of the variance components in a cluster sample are dramatically affected by how much the clusters vary in size, by the type of sample design, and by the form of estimator used. Measures of the homogeneity of survey variables within clusters are related to the variance components and affect the numbers of sample units that should be selected at each stage to achieve the desired precision levels. Measures of homogeneity can be estimated using standard software for random-effects models but the model-based intracluster correlations may need to be transformed to be appropriate for use with the sample design. We illustrate these points and implications for sample size calculation for two-stage sample designs using a realistic population derived from household surveys and the decennial census in the U.S.
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Barbosa, Tiago M., Jorge E. Morais, Mário J. Costa, José Goncalves, Daniel A. Marinho, and António J. Silva. "Young Swimmers’ Classification Based on Kinematics, Hydrodynamics, and Anthropometrics." Journal of Applied Biomechanics 30, no. 2 (April 2014): 310–15. http://dx.doi.org/10.1123/jab.2013-0038.
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The aim of this article has been to classify swimmers based on kinematics, hydrodynamics, and anthropometrics. Sixty-seven young swimmers made a maximal 25 m front-crawl to measure with a speedometer the swimming velocity (v), speed-fluctuation (dv) anddvnormalized tov(dv/v). Another two 25 m bouts with and without carrying a perturbation device were made to estimate active drag coefficient (CDa). Trunk transverse surface area (S) was measured with photogrammetric technique on land and in the hydrodynamic position. Cluster 1 was related to swimmers with a high speed fluctuation (ie,dvanddv/v), cluster 2 with anthropometrics (ie,S) and cluster 3 with a high hydrodynamic profile (ie,CDa). The variable that seems to discriminate better the clusters was thedv/v(F= 53.680;P< .001), followed by thedv(F= 28.506;P< .001),CDa(F= 21.025;P< .001),S(F= 6.297;P< .01) andv(F= 5.375;P= .01). Stepwise discriminant analysis extracted 2 functions: Function 1 was mainly defined bydv/vandS(74.3% of variance), whereas function 2 was mainly defined byCDa(25.7% of variance). It can be concluded that kinematics, hydrodynamics and anthropometrics are determinant domains in which to classify and characterize young swimmers’ profiles.
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Magnussen, S., R. E. McRoberts, and E. O. Tomppo. "A resampling variance estimator for the k nearest neighbours technique." Canadian Journal of Forest Research 40, no. 4 (April 2010): 648–58. http://dx.doi.org/10.1139/x10-020.
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Current estimators of variance for the k nearest neighbours (kNN) technique are designed for estimates of population totals. Their efficiency in small-area estimation problems can be poor. In this study, we propose a modified balanced repeated replication estimator of variance (BRR) of a kNN total that performs well in small-area estimation problems and under both simple random and cluster sampling. The BRR estimate of variance is the sum of variances and covariances of unit-level kNN estimates in the area of interest. In Monte Carlo simulations of simple random and cluster sampling from seven artificial populations with real and simulated forest inventory data, the agreement between averages of BRR estimates of variance and Monte Carlo sampling variances was good both for population and for small-area totals. The modified BRR estimator is currently limited to sample sizes no larger than 1984. An accurate approximation to the proposed BRR estimator allows significant savings in computing time.
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Li, Fan, and Guangyu Tong. "Sample size estimation for modified Poisson analysis of cluster randomized trials with a binary outcome." Statistical Methods in Medical Research 30, no. 5 (April 7, 2021): 1288–305. http://dx.doi.org/10.1177/0962280221990415.
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The modified Poisson regression coupled with a robust sandwich variance has become a viable alternative to log-binomial regression for estimating the marginal relative risk in cluster randomized trials. However, a corresponding sample size formula for relative risk regression via the modified Poisson model is currently not available for cluster randomized trials. Through analytical derivations, we show that there is no loss of asymptotic efficiency for estimating the marginal relative risk via the modified Poisson regression relative to the log-binomial regression. This finding holds both under the independence working correlation and under the exchangeable working correlation provided a simple modification is used to obtain the consistent intraclass correlation coefficient estimate. Therefore, the sample size formulas developed for log-binomial regression naturally apply to the modified Poisson regression in cluster randomized trials. We further extend the sample size formulas to accommodate variable cluster sizes. An extensive Monte Carlo simulation study is carried out to validate the proposed formulas. We find that the proposed formulas have satisfactory performance across a range of cluster size variability, as long as suitable finite-sample corrections are applied to the sandwich variance estimator and the number of clusters is at least 10. Our findings also suggest that the sample size estimate under the exchangeable working correlation is more robust to cluster size variability, and recommend the use of an exchangeable working correlation over an independence working correlation for both design and analysis. The proposed sample size formulas are illustrated using the Stop Colorectal Cancer (STOP CRC) trial.
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Milosavljević, Miloš, Christopher J. Miller, Steven R. Furlanetto, and Asantha Cooray. "Cluster Merger Variance and the Luminosity Gap Statistic." Astrophysical Journal 637, no. 1 (January 17, 2006): L9—L12. http://dx.doi.org/10.1086/500547.
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De Mulder, Wim. "Instability and cluster stability variance for real clusterings." Information Sciences 260 (March 2014): 51–63. http://dx.doi.org/10.1016/j.ins.2013.11.022.
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Kim, Seung-Gu. "Identification of Cluster with Composite Mean and Variance." Communications for Statistical Applications and Methods 18, no. 3 (May 31, 2011): 391–401. http://dx.doi.org/10.5351/ckss.2011.18.3.391.
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Crespi, Catherine M., Weng Kee Wong, and Sheng Wu. "A new dependence parameter approach to improve the design of cluster randomized trials with binary outcomes." Clinical Trials 8, no. 6 (November 2, 2011): 687–98. http://dx.doi.org/10.1177/1740774511423851.
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Background and Purpose Power and sample size calculations for cluster randomized trials require prediction of the degree of correlation that will be realized among outcomes of participants in the same cluster. This correlation is typically quantified as the intraclass correlation coefficient (ICC), defined as the Pearson correlation between two members of the same cluster or proportion of the total variance attributable to variance between clusters. It is widely known but perhaps not fully appreciated that for binary outcomes, the ICC is a function of outcome prevalence. Hence, the ICC and the outcome prevalence are intrinsically related, making the ICC poorly generalizable across study conditions and between studies with different outcome prevalences. Methods We use a simple parametrization of the ICC that aims to isolate that part of the ICC that measures dependence among responses within a cluster from the outcome prevalence. We incorporate this parametrization into sample size calculations for cluster randomized trials and compare our method to the traditional approach using the ICC. Results Our dependence parameter, R, may be less influenced by outcome prevalence and has an intuitive meaning that facilitates interpretation. Estimates of R from previous studies can be obtained using simple statistics. Comparison of methods showed that the traditional ICC approach to sample size determination tends to overpower studies under many scenarios, calling for more clusters than truly required. Limitations The methods are developed for equal-sized clusters, whereas cluster size may vary in practice. Conclusions The dependence parameter R is an alternative measure of dependence among binary outcomes in cluster randomized trials that has a number of advantages over the ICC.
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Markos, Daniel, Girma Mammo, and Walelign Worku. "Principal component and cluster analyses based characterization of maize fields in southern central Rift Valley of Ethiopia." Open Agriculture 7, no. 1 (January 1, 2022): 504–19. http://dx.doi.org/10.1515/opag-2022-0105.
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Abstract Soil management decisions should consider physical potential of the environment, weather variability, and requirements of crops to maximize production to the potential limits. This calls for characterization of environments using selected input variables. Such studies are scanty in southern central Rift Valley of Ethiopia due to which the area is considered homogeneous and identical for agricultural planning, extension, and input delivery programs. Thus, to investigate the scenario, we employed principal component, clustering, and GIS analysis on geo-referenced physiographic and climatic attributes, and their statistical variables obtained from 43 stations with the objective of identifying homogeneous management units with similar physiography, weather pattern, and production scheduling. The analysis of principal components (PCs) indicated that three PCs explained 74.7% of variance in October, November, December, and January (ONDJ), four PCs explained 79.3% of variance in February, March, April, and May, and four PCs explained 80.5% of variance in June, July, August, and September (JJAS). Cluster-I was characterized by high altitude and low temperature in ONDJ season. Cluster-II was characterized by low altitude and high temperature across most seasons. Cluster-III was intermediate in altitude, temperature, and rainfall. Cluster-IV was characterized by high rainfall in JJAS. In all the clusters, PC1 was the mean rainfall component with strong association with altitude and longitude, while PC2 was the temperature component. PC3 is the statistical component with strong influence from mean rainfall. Thus the factors that determine the formation of clusters are reduced from 12 to 5 (T mean, latitude, longitude, altitude, and RFmean) and 43 stations are grouped into 4 clusters (Shamana, Bilate, Hawassa, and Dilla) which are geographically and ecologically distinct. These clusters require different sets of agro-meteorology advisory, maize management, and input delivery strategies.
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Biswas, A., U. Sarker, BR Banik, MM Rohman, and MA Khaleque Mian. "Genetic divergence study in salinity stress tolerant maize (Zea mays L.)." Bangladesh Journal of Agricultural Research 39, no. 4 (March 12, 2015): 621–30. http://dx.doi.org/10.3329/bjar.v39i4.22540.
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The study was conducted to investigate the genetic diversity of some maize inbreds under salinity stress condition using Mahalanobiss statistic (D2) and principal component analysis. Analysis of variance showed significant difference for all the characters. Results of multivariate analysis revealed that seventeen inbred lines formed five clusters at 12 dS level of salinity. The highest intra-cluster distance was recorded in cluster IV containing three genotypes and the lowest was in cluster V having one genotype. The inter cluster D2 values revealed maximum distance among the clusters. The highest inter cluster distance was observed between clusters IV & III and lowest was between V & I. Cluster IV had the highest cluster means for cob height, tassel length, cob length, SPAD value, number of seeds/cob, 100 seed weight, cob diameter and grain yield per plant. Considering cluster distance, inter-genotypic distance, cluster mean and other agronomic performances the genotypes CZ29, CZ33 and P43 from cluster IV and E135, E158, E169, P29 and P45 from cluster III may be considered as better parents for future hybridization programs to obtain desirable segregates in respect of different yield and yield contributing characters under salinity stress. DOI: http://dx.doi.org/10.3329/bjar.v39i4.22540 Bangladesh J. Agril. Res. 39(4): 621-630, December 2014
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Prasad, B., M. A. Babar, X. Y. Xu, G. H. Bai, and A. R. Klatt. "Genetic diversity in the U.S. hard red winter wheat cultivars as revealed by microsatellite markers." Crop and Pasture Science 60, no. 1 (2009): 16. http://dx.doi.org/10.1071/cp08052.
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Knowledge of the genetic diversity existing in previously released hard red winter wheat (HRWW, Triticum aestivum L.) cultivars in the Great Plains region, United States, is essential for effective utilisation of these genetic resources in the various HRWW breeding programs. To ascertain a measure of the genetic diversity of the existing US HRWW, 60 cultivars were analysed with 62 microsatellite markers distributed throughout the wheat genome. Marker data were subjected to distance-based analysis and analysis of molecular variances. In total, 341 polymorphic alleles were scored with a range of 2–12 alleles per locus. Genetic diversity gradually increased in cultivars released after the 1970s. Cultivars released in the 1990s had the highest allelic richness (4.79), gene diversity (0.60), and polymorphic information content (0.56). Levels of genetic diversity were similar between the major HRWW breeding programs. Cluster analysis resulted in eight clusters. Cluster grouping gave close matches with pedigrees and with regional distribution of the cultivars. Using decadal information, cultivars released from 1900–1969 were grouped into one cluster, cultivars from 1990–2005 were grouped into a separate cluster, whereas cultivars from the 1980s did not group with any other decades. Analysis of molecular variance revealed a significant variation among the clusters, signifying that a true genetic variation existed among the clusters. The higher proportion of genetic variation explained by cultivars within clusters compared with among clusters indicates greater genetic diversity among cultivars within clusters. Our results indicate that genetic diversity of Great Plains HRWW cultivars has increased in the past century, and the trend is continuing.
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Muthahharah, Isma, and Agusalim Juhari. "A Cluster Analysis with Complete Linkage and Ward's Method for Health Service Data in Makassar City." Jurnal Varian 4, no. 2 (April 30, 2021): 109–16. http://dx.doi.org/10.30812/varian.v4i2.883.
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Health care facilities are a place used to organize health efforts. Health service data in Makassar City has not shown which sub-districts have excellent service criteria, good enough, and not good. Therefore, it is necessary to group sub-districts with cluster analysis using hierarchy method. The hierarchy method used in this study is only 2, namely complete linkage and ward's method. Complete linkage method is the opposite of the approach to the minimum distance principle that is the furthest distance between objects while Ward's Method is a method that aims to minimize variance between objects in one cluster. There are four health services used, namely Hospitals, Health Centers, Home Care and Telemedicine with 15 sub-districts. This study also used a validity test namely Index Davies Bouldin (IDB) to determine the criteria of health services. The results of the analysis on complete linkage formed 3 clusters, namely cluster 1 with good health services, cluster 2 with excellent health services, and cluster 3 with poor health services. In addition, ward's Method also formed 3 clusters, namely cluster 1 with good health services, clusters 2 with poor service, and cluster 3 with excellent health services.
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Klein, Matthias, Holger Israel, Aarti Nagarajan, Frank Bertoldi, Florian Pacaud, Adrian T. Lee, Martin Sommer, and Kaustuv Basu. "Weak lensing measurements of the APEX-SZ galaxy cluster sample." Monthly Notices of the Royal Astronomical Society 488, no. 2 (June 3, 2019): 1704–27. http://dx.doi.org/10.1093/mnras/stz1491.
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ABSTRACT We present a weak lensing analysis for galaxy clusters from the APEX-SZ survey. For 39 massive galaxy clusters that were observed via the Sunyaev–Zel’dovich effect (SZE) with the APEX telescope, we analyse deep optical imaging data from WFI(@2.2mMPG/ESO) and Suprime-Cam(@SUBARU) in three bands. The masses obtained in this study, including an X-ray selected sub-sample of 27 clusters, are optimized for and used in studies constraining the mass to observable scaling relations at fixed cosmology. A novel focus of our weak lensing analysis is the multicolour background selection to suppress effects of cosmic variance on the redshift distribution of source galaxies. We investigate the effects of cluster member contamination through galaxy density, shear profile, and recovered concentrations. We quantify the impact of variance in source redshift distribution on the mass estimate by studying nine sub-fields of the COSMOS survey for different cluster redshift and magnitude limits. We measure a standard deviation of ∼6 per cent on the mean angular diameter distance ratio for a cluster at z = 0.45 and shallow imaging data of R ≈ 23 mag. It falls to ∼1 per cent for deep, R = 26 mag, observations. This corresponds to 8.4 per cent and 1.4 per cent scatter in M200. Our background selection reduces this scatter by 20−40 per cent, depending on cluster redshift and imaging depth. We derived cluster masses with and without using a mass concentration relation and find consistent results, and concentrations consistent with the used mass–concentration relation.
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Akther, CA, M. Hasan, MS Raihan, MM Hossain, and MAK Mian. "Genetic Divergence in Stem Amaranth (Amaranthus tricolor L.) Genotypes for Yield and its Component Characters." Agriculturists 11, no. 1 (June 10, 2013): 82–88. http://dx.doi.org/10.3329/agric.v11i1.15247.
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An investigation was carried out to identify the extent of genetic divergence that exist for the yield and yield contributing characters of seventeen genotypes of amaranth using Mahalanobis D2 analysis. Analysis of variance showed significant difference among the genotypes for most of the characters studied. The genotypes under study fell into 4 clusters. The distribution pattern indicated that the maximum number of genotypes (6) was included in cluster (IV) followed by cluster III (5) and cluster II (5), and the minimum number was in cluster I (1). The inter cluster distance in most of the cases was higher than the intra cluster distance, which indicated wider genetic diversity among the accessions of different groups. The highest inter cluster distance was observed between IV and I, followed by the distance between cluster II and I showing wide diversity among the groups. The lowest inter-cluster distance was observed between clusters III and II suggesting a close relationship among the genotypes of these two clusters. The highest intra-cluster distance was observed for the cluster IV and the lowest for the cluster I. The positive values of vector I and vector 2 for stem weight and weight of leaf indicated that these two characters had the highest contribution towards the divergence among the stem amaranths. The genotypes of stem amaranth from cluster I and cluster IV may be selected as parents in future hybridization program. DOI: http://dx.doi.org/10.3329/agric.v11i1.15247 The Agriculturists 2013; 11(1) 82-88
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Hobbs, M., M. J. Duncan, P. Collins, J. Mckenna, S. Schoeppe, A. L. Rebar, S. Alley, C. Short, and C. Vandelanotte. "Clusters of health behaviours in Queensland adults are associated with different socio-demographic characteristics." Journal of Public Health 41, no. 2 (March 13, 2018): 268–77. http://dx.doi.org/10.1093/pubmed/fdy043.
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Abstract Background The co-occurrence of unhealthy lifestyles, calls for interventions that target multiple health behaviours. This study investigates the clustering of health behaviours and examines demographic differences between each cluster. Methods In total, 934 adults from Queensland, Australia completed a cross-sectional survey assessing multiple health behaviours. A two-step hierarchical cluster analysis using multiple iterations identified the optimal number of clusters and the subset of distinguishing health behaviour variables. Univariate analyses of variance and chi-squared tests assessed difference in health behaviours by socio-demographic factors and clusters. Results Three clusters were identified: the ‘lower risk’ cluster (n = 436) reported the healthiest profile and met all public health guidelines. The ‘elevated risk’ cluster (n = 105) reported a range of unhealthy behaviours such as excessive alcohol consumption, sitting time, fast-food consumption, smoking, inactivity and a lack of fruit and vegetables. The ‘moderate risk behaviour’ cluster (n = 393) demonstrated some unhealthy behaviours with low physical activity levels and poor dietary outcomes. The ‘elevated risk’ cluster were significantly younger and more socio-economically disadvantaged than both the ‘lower and moderate risk’ clusters. Discussion Younger people who live in more deprived areas were largely within the ‘elevated risk’ cluster and represent an important population for MHBC interventions given their wide range of unhealthy behaviours.
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Shoji, Tomokazu, Natsu Sato, Haruhisa Fukuda, Yuichi Muraki, Keishi Kawata, and Manabu Akazawa. "Clinical Implication of the Relationship between Antimicrobial Resistance and Infection Control Activities in Japanese Hospitals: A Principal Component Analysis-Based Cluster Analysis." Antibiotics 11, no. 2 (February 10, 2022): 229. http://dx.doi.org/10.3390/antibiotics11020229.
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There are few multicenter investigations regarding the relationship between antimicrobial resistance (AMR) and infection-control activities in Japanese hospitals. Hence, we aimed to identify Japanese hospital subgroups based on facility characteristics and infection-control activities. Moreover, we evaluated the relationship between AMR and hospital subgroups. We conducted a cross-sectional study using administrative claims data and antimicrobial susceptibility data in 124 hospitals from April 2016 to March 2017. Hospitals were classified using cluster analysis based the principal component analysis-transformed data. We assessed the relationship between each cluster and AMR using analysis of variance. Ten variables were selected and transformed into four principal components, and five clusters were identified. Cluster 5 had high infection control activity. Cluster 2 had partially lower activity of infection control than the other clusters. Clusters 3 and 4 had a higher rate of surgeries than Cluster 1. The methicillin-resistant Staphylococcus aureus (MRSA)/S. aureus detection rate was lowest in Cluster 1, followed, respectively, by Clusters 5, 2, 4, and 3. The MRSA/S. aureus detection rate differed significantly between Clusters 4 and 5 (p = 0.0046). Our findings suggest that aggressive examination practices are associated with low AMR whereas surgeries, an infection risk factor, are associated with high AMR.
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Rohman, MM, BR Banik, A. Biswas, and MS Rahman. "Genetic diversity of maize (Zea mays L.) Inbreds under salinity stress." Bangladesh Journal of Agricultural Research 40, no. 4 (March 2, 2016): 529–36. http://dx.doi.org/10.3329/bjar.v40i4.26928.
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The study was conducted to investigate the genetic diversity of some maize inbreds under salinity stress condition using Mahalanobiss statistic (D2) and principal component analysis. Analysis of variance showed significant difference for all the characters. Results of multivariate analysis revealed that twenty five inbred lines formed five clusters at 8 dS level of salinity. The highest intra-cluster distance was recorded in cluster III containing eight genotypes and the lowest was in cluster II having one genotype. The highest inter cluster distance was observed between clusters II & V and lowest was between I & III. Cluster II had the highest cluster means for plant height, cob height, above ground dry mass, cob per plant, cob length, and grain yield per plant. Considering cluster distance, inter-genotypic distance and other agronomic performances the genotypes CZ12, CZ19, CZ26, CZ29, CZ31, CZ32, CZ33 & CML470 from cluster III and CZ27, CZ37, CML251 and CML456 from cluster V may be considered as better parents for future hybridization programs to obtain desirable segregate in respect of different yield and yield contributing characters under salinity stress.Bangladesh J. Agril. Res. 40(4): 529-536, December 2015