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The site regression model (SREG) is utilized by plant breeders for analyzing multi-environment trials (MET) to examine the relationships among test environments, genotypes (G), and genotype-by-environment interactions (GE). SREG explores a matrix of G and GE by performing a singular value decomposition on the residuals matrix from a one-way ANOVA, requiring complete data. As missing values are common in MET, we propose two new imputation methods that implement an Expectation Maximization algorithm to fit the SREG model. To evaluate the impact on SREG model parameter estimation of these proposed methods and other competing imputation methods available, we conducted two studies using different approaches. One study involved simulated data while the other used a real dataset. In both studies, different measures to verify whether the joint effect of G plus GE is altered by imputation of data, and the reproducibility of missing data were evaluated. We also incorporated situations not commonly addressed in the literature, such as non-random structures of missing values and a big data situation. The proposed procedures provided estimators with good performance, maintaining superiority in several aspects studied, even when the competing imputation methods did not achieve convergence. Therefore, the new methods enabled incomplete MET data to be effectively analyzed by a SREG model. [ABSTRACT FROM AUTHOR]

Multivariate capability analysis has been the focus of study in recent years, during which many authors have proposed different multivariate capability indices. In the operative context, capability indices are used as measures of the ability of the process to operate according to specifications. Because the numerical value of the index is used to conclude about the capability of the process, it is essential to bear in mind that almost always that value is obtained from a sample of process units. Therefore, it is really necessary to know the properties that the indices have when they are calculated on sampling information, in order to assess the goodness of the inferences made from them. In this work, we conduct a simulation study to investigate distributional properties of two existing indices: NMCpm index based on ratio of volumes and Mp2 index based on principal component analysis. We analyze the relative bias and the mean square error of the estimators of the indices, and we also obtain their empirical distributions that are used to estimate the probability that the indices classify correctly a process as capable or as incapable. The results allow us to recommend the use of one of these indices, as it has shown better properties. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In the context of process capability analysis, the results of most processes are dominated by two or even more quality characteristics, so that the assessment of process capability requires that all of them are considered simultaneously. In recent years, many researchers have developed different alternatives of multivariate capability indices using different approaches of construction. In this paper, four of them are compared through the study of their ability to correctly distinguish capable processes from incapable processes under a diversity of simulated scenarios, defining suitable minimum desirable values that allow to decide whether the process meets or does not meet specifications. In this sense, properties analyzed can be seen as sensitivity and specificity, assuming that a measure is sensitive if it can detect the lack of capability when it actually exists and specific if it correctly identifies capable processes. Two indices based on ratios of regions and two based on the principal component analysis have been selected for the study. The scenarios take into account several joint distributions for the quality variables, normal and non-normal, several numbers of variables, and different levels of correlation between them, covering a wide range of possible situations. The results showed that one of the indices has better properties across most scenarios, leading to right conclusions about the state of capability of processes and making it a recommendable option for its use in real-world practice. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Multivariate hypothesis testing in studies of vegetation is likely to be hindered by unrealistic assumptions when based on conventional statistical methods. This can be overcome by randomization tests. In this paper, the accuracy and power of a MANOVA randomization test are evaluated for one and two factors with interaction with simulated data from three distributions. The randomization test is based on the partitioning of sum of squares computed from Euclidean distances. In one-factor designs, sample size and variance inequality were evaluated. The results showed a high level of accuracy. The power curve was higher with normal distribution, lower with uniform, intermediate with lognormal and was sensitive to variance inequality. In two-factor designs, three methods of permutations and two statistics were compared. The results showed that permutation of the residuals with Fpseudo is accurate and can give good power for testing the interaction and restricted permutation for testing main factors. [ABSTRACT FROM AUTHOR]

Observations with missing data are a typical predicament in the context of multivariate statistical process control (MSPC). When process control is performed using a T2 control chart of the principal components (PCs), several score imputation methods have been proposed. Some of these lead to estimators with good properties. However, there are no detailed studies pertaining the performance of Phase II Hotelling's T2 and squared prediction error (SPE) charts when such imputation methods are used. In this paper, a simulation study was conducted to assess the consequences of the estimation of incomplete observations using score imputation methods on T2 and SPE control charts. The study involves several scenarios that combine different correlation structures for the PCA model, methods of score estimation, percentages of missing data and patterns of incomplete information. Results show that the charts' standard control limits are adequate only for small percentages of missing values and that their average run lengths (ARLs) tend to be larger than expected in out‐of‐control situations. To illustrate the conclusions of the study, we present two examples. Our findings lead us to suggest a modification that may result in an improvement in the performance of the T2 and SPE control charts. [ABSTRACT FROM AUTHOR]

Objective: To study the ability of the urea/creatinine index to identify severe protein catabolism from the isolated urine of critically ventilated patients. Methods: This was a prospective, observational study. It included 52 patients without kidney failure. Variables: total urinary nitrogen estimated from the urea in 24-hour urine on the second (T1) and fourth days (T2) and urea/creatinine index in isolated urine before 24-hour urine collection. Results: Severe protein hypercatabolism (estimated total urinary nitrogen > 15g) was present in 14 patients (26.9%) at T1 and in 29 (55.7%) at T2. Eighty-four percent of patients had low nutritional risk by the Nutrition Risk in the Critically Ill score. At T1, the Pearson correlation between the estimated total urinary nitrogen and the urea/creatinine index was 0.272 (p = 0.051), and at T2 it was 0.276 (p = 0.048). The urea/creatinine index at T2 had a tendency to better discriminate severe protein hypercatabolism than Acute Physiology and Chronic Health Evaluation II and Nutrition Risk in the Critically Ill (AUC 0.741 versus 0.669 and 0.656, 95%CI: 0.602 - 0.880; 0.519 - 0.818 and 0.506 - 0.806, respectively). The optimal cutoff value of the urea/creatinine index for the diagnosis of severe protein hypercatabolism was 16.15, with a sensitivity of 79.31% (95%CI: 59.74 - 91.29), specificity of 60.87% (95%CI: 38.78 - 79.53), positive predictive value 71.88% (95%CI: 53.02 - 85.60), negative predictive value 70.0% (95%CI: 45.67 - 87.18), LR (+) 2.03 (95%CI: 1.18 - 3.49), and LR (-) 0.34 (95%CI: 0.16 - 0.74). Conclusion: The urea/creatinine index measured on the fourth day has a certain ability to estimate severe protein hypercatabolism (as defined by estimated total urinary nitrogen) but does not replace total urinary nitrogen in critically ventilated patients without kidney failure. Due to its reasonable sensitivity, it could be used as a screen to identify which patients to take a 24-hour urine sample from. [ABSTRACT FROM AUTHOR]