For multivariate linear regression what skew and kurtosis thresholds are considered valid?4 answersIn multivariate linear regression, valid skewness and kurtosis thresholds are crucial for assessing the error distribution. Various studies have explored this aspect. Dufour et al. (Context_1) developed tests for error distribution in MLR models, comparing empirical skewness and kurtosis criteria with simulated expected values. Machado (Context_2) examined measures of multivariate skewness and kurtosis, providing methods for calculating their distributions. Bali et al. (Context_3) focused on high-order moments in estimating conditional value at risk, emphasizing the significance of time-varying parameters. Additionally, Cain et al. (Context_5) highlighted the prevalence of nonnormality in multivariate data, suggesting the routine reporting of skewness and kurtosis. These studies collectively underscore the importance of considering skewness and kurtosis thresholds in multivariate linear regression for robust error distribution assessment.
Doubly truncated estimation4 answersDoubly truncated estimation refers to the estimation of a random variable that is sampled under random double truncation. Different methods have been proposed for this type of estimation. One approach is kernel-type density estimation, where estimators are defined as a convolution between a kernel function and an estimator of the cumulative distribution function. Another method is based on a generalization of Kendall's tau test and the Mann-Whitney-type rank estimator, which can be easily computed using existing software packages. Bayesian nonparametric density estimation using a Pólya tree prior has also been proposed, allowing for truncated regression as well. Additionally, an extension of the nonparametric maximum likelihood estimator has been introduced, which takes into account the dependence between the variable of interest and the truncation variables.
What is the breakdown of R?4 answersR is an implementation of the object-oriented mathematical programming language S. It is developed by statisticians around the world and is free software. R is capable of running high powered statistical simulations, producing elegant graphics, and computing econometric estimates for a wide array of popular estimators. It can seamlessly integrate with LTEX to construct fully reproducible scientific research. The essence of R is a language for data analysis and graphics, as defined by Ross Ihaka and Robert Gentleman in their 1996 paper. However, it is difficult to encapsulate such a diverse programming language into a single phrase.
What are the strenghts of multivariate models?3 answersMultivariate models have several strengths. They can better characterize region-by-region interactions compared to univariate approaches, as they consider the spatial patterns of voxel-wise signals within individual nodes. Multivariate models exhibit higher reliability at both the edge-level and connectome-level, making them more suitable for predicting individual differences. They also show greater sensitivity to brain states within individuals, providing more powerful information about an individual's functional brain organization. In addition, multivariate models have many attractive features for neuroimaging data analysis. They evaluate correlation/covariance of activation across brain regions, allowing for easier interpretation as a signature of neural networks. Multivariate approaches can result in greater statistical power and better reproducibility checks compared to univariate techniques. They also lend themselves better to prospective application of results to new datasets. However, the high barrier of entry to using multivariate approaches prevents more widespread application in the community.
What are some of the challenges in developing and using multivariate models?5 answersDeveloping and using multivariate models pose several challenges. One challenge is selecting appropriate distributions that can effectively capture the complexity of the data. Another challenge is accurately estimating the model parameters, which can be computationally complex and prone to local maxima. Additionally, determining the optimal number of components in the model is a significant challenge, with approaches like minimum message length being used. Multiscale modeling, which involves linking events across different time and length scales, presents another challenge due to computational limitations and the difficulty in coupling different modeling methods. Finally, consolidating data of various types, from different sources, and across different time frames or scales is a critical challenge for statisticians, as it requires a suitable framework for analysis and inference.
What are the limitations of the FMOLS estimator?2 answersThe limitations of the FMOLS estimator include the complexity and technical nature of the numerical approaches used in maximum likelihood estimation (MLE). This complexity can create barriers, anxieties, and uncertainties among users, increasing the risk of misinterpretation of study findings. Additionally, textbooks often provide oversimplified descriptions of MLE, omitting important details from the discussion. These untold stories about MLE further contribute to the challenges faced by users.