Univariate and multivariate process yield indices based on location-scale family of distributions

Several measures of process yield, defined on univariate and multivariate normal process characteristics, have been introduced and studied by several authors. These measures supplement several well-known Process Capacity Indices (PCI) used widely in assessing the quality of products before being released into the marketplace. In this paper, we generalise these yield indices to the location-scale family of distributions which includes the normal distribution as one of its member. One of the key contributions of this paper is to demonstrate that under appropriate conditions, these indices converge in distribution to a normal distribution. Several numerical examples will be used to illustrate our procedures and show how they can be applied to perform statistical inferences on process capability.

Unite and conquer: univariate and multivariate approaches for finding differentially expressed gene sets

Motivation: Recently, many univariate and several multivariate approaches have been suggested for testing differential expression of gene sets between different phenotypes. However, despite a wealth of literature studying their performance on simulated and real biological data, still there is a need to quantify their relative performance when they are testing different null hypotheses.

Results: In this article, we compare the performance of univariate and multivariate tests on both simulated and biological data. In the simulation study we demonstrate that high correlations equally affect the power of both, univariate as well as multivariate tests. In addition, for most of them the power is similarly affected by the dimensionality of the gene set and by the percentage of genes in the set, for which expression is changing between two phenotypes. The application of different test statistics to biological data reveals that three statistics (sum of squared t-tests, Hotelling's T2, N-statistic), testing different null hypotheses, find some common but also some complementing differentially expressed gene sets under specific settings. This demonstrates that due to complementing null hypotheses each test projects on different aspects of the data and for the analysis of biological data it is beneficial to use all three tests simultaneously instead of focusing exclusively on just one.

Comparison of the univariate and multivariate methods in the optimization of experimental conditions for determining Cu, Pb, Ni and Cd in biodiesel by GFAAS

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Assessment of agroeconomic indices in polycultures of lettuce, rocket and carrot through uni- and multivariate approaches in semi-arid Brazil

The polyculture among vegetables is an activity that to have good results, needs a proper planning. Although it often requires more labor, has several advantages over monoculture, among them is that polycultures are generally are more productive, provide with productivity of various plant constituents and a more balanced human diet, contribute to economic return, economic and yield stability, social benefits and farmer's direct participation in decision-making. The objective of this study was to evaluate agroeconomic indices of polycultures derived from the combination of two cultivars of lettuce with two cultivars of rocket in two cultures strip-intercropped with carrot cultivar 'Brasilia' through uni-multivariate approaches in semi-arid Brazil. The experimental design used was of randomized complete blocks with five replications and the treatments arranged in a factorial scheme of 2 x 2. The treatments consisted of the combination of two lettuce cultivars (Baba de Verao and Taina) with two rocket cultivars (Cultivada and Folha Larga) in two cultures associated with carrot cv. Brasilia. hi each block were grown plots with two lettuce cultivars and two rocket cultivars, and carrot in sole crop. In each system was determined the lettuce leaf yield, rocket green mass yield and carrot commercial yield. Agrieconomic indices such as operational cost, gross and net income, monetary advantage, rate of return, profit margin, land equivalent ratio and yield efficiency for DEA were used to measure the efficiency of intercropping systems. In the bicropping of lettuce and rocket associated with carrot cv. 'Brasilia', suggests the use of lettuce cultivar 'Taina' combined with rocket cultivars 'Cultivada' or 'Folha Larga'. It was observed significant effect of lettuce cultivars in the evaluation of polycultures of lettuce, carrot and rocket, with strong expression for the lettuce cultivar 'Taina'. Both uni- and multivariate approaches were effective in the discrimination of the best polycultures. (C) 2011 Elsevier Ltd. All rights reserved.

Univariate, bivariate, and multivariate methods in corpus-based lexicography : A study of synonymy

In this dissertation, I present an overall methodological framework for studying linguistic alternations, focusing specifically on lexical variation in denoting a single meaning, that is, synonymy. As the practical example, I employ the synonymous set of the four most common Finnish verbs denoting THINK, namely ajatella, miettiä, pohtia and harkita ‘think, reflect, ponder, consider’. As a continuation to previous work, I describe in considerable detail the extension of statistical methods from dichotomous linguistic settings (e.g., Gries 2003; Bresnan et al. 2007) to polytomous ones, that is, concerning more than two possible alternative outcomes. The applied statistical methods are arranged into a succession of stages with increasing complexity, proceeding from univariate via bivariate to multivariate techniques in the end. As the central multivariate method, I argue for the use of polytomous logistic regression and demonstrate its practical implementation to the studied phenomenon, thus extending the work by Bresnan et al. (2007), who applied simple (binary) logistic regression to a dichotomous structural alternation in English. The results of the various statistical analyses confirm that a wide range of contextual features across different categories are indeed associated with the use and selection of the selected think lexemes; however, a substantial part of these features are not exemplified in current Finnish lexicographical descriptions. The multivariate analysis results indicate that the semantic classifications of syntactic argument types are on the average the most distinctive feature category, followed by overall semantic characterizations of the verb chains, and then syntactic argument types alone, with morphological features pertaining to the verb chain and extra-linguistic features relegated to the last position. In terms of overall performance of the multivariate analysis and modeling, the prediction accuracy seems to reach a ceiling at a Recall rate of roughly two-thirds of the sentences in the research corpus. The analysis of these results suggests a limit to what can be explained and determined within the immediate sentential context and applying the conventional descriptive and analytical apparatus based on currently available linguistic theories and models. The results also support Bresnan’s (2007) and others’ (e.g., Bod et al. 2003) probabilistic view of the relationship between linguistic usage and the underlying linguistic system, in which only a minority of linguistic choices are categorical, given the known context – represented as a feature cluster – that can be analytically grasped and identified. Instead, most contexts exhibit degrees of variation as to their outcomes, resulting in proportionate choices over longer stretches of usage in texts or speech.

Estimating capability index in multivariate processes using bootstrap sequential sampling procedures

Capability indices in both univariate and multivariate processes are extensively employed in quality control to assess the quality status of production batches before their release for operational use. It is traditionally a measure of the ratio of the allowable process spread and the actual spread. In this paper, we will adopt a bootstrap and sequential sampling procedures to determine the optimal sample size for estimating a multivariate capability index introduced by Pearns et. al. [12]. Bootstrap techniques have the distinct advantage of placing very minimum requirement on the distributions of the underlying quality characteristics, thereby rendering them more relevant under a wide variety of situations. Finally, we provide several numerical examples where the sequential sampling procedures are evaluated and compared.

Selection indices and multivariate analysis show similar results in the evaluation of growth and carcass traits in beef cattle.

2016

Pattern analysis of the diversity of morphological plant attributes and herbage yield in a world collection of white clover (Trifolium repens L.) germplasm characterised in a summer moisture stress environment of Australia

Information on the variation available for different plant attributes has enabled germplasm collections to be effectively utilised in plant breeding. A world sourced collection of white clover germplasm has been developed at the White Clover Resource Centre at Glen Innes, New South Wales. This collection of 439 accessions was characterised under field conditions as a preliminary study of the genotypic variation for morphological attributes; stolon density, stolon branching, number of nodes. number of rooted nodes, stolon thickness, internode length, leaf length, plant height and plant spread, together with seasonal herbage yield. Characterisation was conducted on different batches of germplasm (subsets of accessions taken from the complete collection) over a period of five years. Inclusion of two check cultivars, Haifa and Huia, in each batch enabled adjustment of the characterisation data for year effects and attribute-by-year interaction effects. The component of variance for seasonal herbage yield among batches was large relative to that for accessions. Accession-by-experiment and accession-by-season interactions for herbage yield were not detected. Accession mean repeatability for herbage yield across seasons was intermediate (0.453). The components of genotypic variance among accessions for all attributes, except plant height, were larger than their respective standard errors. The estimates of accession mean repeatability for the attributes ranged from low (0.277 for plant height) to intermediate (0.544 for internode length). Multivariate techniques of clustering and ordination were used to investigate the diversity present among the accessions in the collection. Both cluster analysis and principal component analysis suggested that seven groups of accessions existed. It was also proposed from the pattern analysis results that accessions from a group characterised by large leaves, tall plants and thick stolons could be crossed with accessions from a group that had above average stolon density and stolon branching. This material could produce breeding populations to be used in recurrent selection for the development of white clover cultivars for dryland summer moisture stress environments in Australia. The germplasm collection was also found to be deficient in genotypes with high stolon density, high number of branches high number of rooted nodes and large leaves. This warrants addition of new germplasm accessions possessing these characteristics to the present germplasm collection.

Comparison between univariate and bivariate geostatistical models for estimating catch per unit of effort (cpue): A simulation study

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Prevalence of the hepatitis C virus among university employees in São Paulo, southeastern Brazil: predictive factors and geoprocessing spatial analysis

Background There are limited studies on the prevalence and risk factors associated with hepatitis C virus (HCV) infection. Objective Identify the prevalence and risk factors for HCV infection in university employees of the state of São Paulo, Brazil. Methods Digital serological tests for anti-HCV have been performed in 3153 volunteers. For the application of digital testing was necessary to withdraw a drop of blood through a needlestick. The positive cases were performed for genotyping and RNA. Chi-square and Fisher’s exact test were used, with P-value <0.05 indicating statistical significance. Univariate and multivariate logistic regression were also used. Results Prevalence of anti-HCV was 0.7%. The risk factors associated with HCV infection were: age >40 years, blood transfusion, injectable drugs, inhalable drugs (InDU), injectable Gluconergam®, glass syringes, tattoos, hemodialysis and sexual promiscuity. Age (P=0.01, OR 5.6, CI 1.4 to 22.8), InDU (P<0.0001, OR=96.8, CI 24.1 to 388.2), Gluconergam® (P=0.0009, OR=44.4, CI 4.7 to 412.7) and hemodialysis (P=0.0004, OR=90.1, CI 7.5 – 407.1) were independent predictors. Spatial analysis of the prevalence with socioeconomic indices, Gross Domestic Product and Human Development Index by the geoprocessing technique showed no positive correlation. Conclusions The prevalence of HCV infection was 0.7%. The independent risk factors for HCV infection were age, InDU, Gluconergan® and hemodialysis. There was no spatial correlation of HCV prevalence with local economic factors.

Effects of a harmful algal bloom on the community ecology, movements and spatial distributions of fishes in a microtidal estuary

Harmful algal blooms can adversely affect fish communities, though their impacts are highly context-dependent and typically differ between fish species. Various approaches, comprising univariate and multivariate analyses and multimetric Fish Community Indices (FCI), were employed to characterise the perceived impacts of a Karlodinium veneficum bloom on the fish communities and ecological condition of the Swan Canning Estuary, Western Australia. The combined evidence suggests that a large proportion of the more mobile fish species in the offshore waters of the bloom-affected area relocated to other regions during the bloom. This was indicated by marked declines in mean species richness, catch rates and FCI scores in the bloom region but concomitant increases in these characteristics in more distal regions, and by pronounced and atypical shifts in the pattern of inter-regional similarities in fish community composition during the bloom. The lack of any significant changes among the nearshore fish communities revealed that bloom impacts were less severe there than in deeper, offshore waters. Nearshore habitats, which generally are in better ecological condition than adjacent offshore waters in this system, may provide refuges for fish during algal blooms and other perturbations, mirroring similar observations of fish avoidance responses to such stressors in estuaries worldwide.