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Articles 1 - 7 of 7
Full-Text Articles in Multivariate Analysis
Hybrid Mixtures Of Factor Analyzers For High Dimensional Data, Kazeem Abiodun Kareem
Hybrid Mixtures Of Factor Analyzers For High Dimensional Data, Kazeem Abiodun Kareem
Dissertations, Master's Theses and Master's Reports
Factor analysis is a powerful tool for modeling latent structures in high-dimensional data, traditional approaches assume a single global structure, limiting their ability to capture heterogeneity. The Mixture of Factor Analyzers (MFA) extends classical factor analysis by modeling data as a mixture of Gaussian-distributed local subspaces, effectively uncovering cluster-specific latent structures. However, MFA relies on Gaussian mixtures, making it sensitive to outliers and ill-suited for heavy-tailed data. The Mixture of $t$-Factor Analyzers (M$t$FA) addresses these limitations by incorporating multivariate $t$-distributions, improving robustness. Despite their advantages, both MFA and M$t$FA face significant computational challenges in high-dimensional settings, particularly due to costly …
Measuring The Performance Of Sdgs In Provincial Level Using Regional Sustainable Development Index, Nurafiza Thamrin, Ika Yuni Wulansari, Puguh Bodro Irawan
Measuring The Performance Of Sdgs In Provincial Level Using Regional Sustainable Development Index, Nurafiza Thamrin, Ika Yuni Wulansari, Puguh Bodro Irawan
Journal of Environmental Science and Sustainable Development
Measuring the national and sub-national progress in achieving such globally adopted development agendas as Sustainable Development Goals (SDGs) is particularly challenging due to data availability and compatibility of indicators to measure SDGs, especially in Indonesia. This paper attempts to measure the performance of sustainable development at the regional level in Indonesia by newly constructing a multidimensional composite index called the Regional Sustainable Development Index (RSDI). RSDI comprises four dimensions, covering comprehensive economic, social, environmental, and governance indicators. By applying factor analysis, the paper assesses the uncertainty of RSDI and the sensitivity of its composing indicators, then further investigates the relationship …
Factors Contributing To The Implementation Of Data Analytics In External Auditing, Jacky Yeamin
Factors Contributing To The Implementation Of Data Analytics In External Auditing, Jacky Yeamin
Student Works (2020-2029)
The objective of the study is to explore the factors effecting the implementation of Data Analytics in Audit process and the impact of those factors on Audit Quality. Two stages of study were performed to achieve the objective of the study, First, this study analysed response letters on the use of Data Analytics (DA) in external auditing submitted by stakeholders of audit services to the International Auditing and Assurance Standards Board (IAASB)’s Data Analytics Working Group (DAWG). Using the Modified IT Audit Model as a framework, this study performs a directed content analysis on all 50 response letters sent to …
The Principal Problem With Principal Components Regression, Gary N. Smith, Heidi Margaret Artigue
The Principal Problem With Principal Components Regression, Gary N. Smith, Heidi Margaret Artigue
Pomona Economics
No abstract provided.
The Principal Problem With Principal Components Regression, Heidi Margaret Artigue, Gary Smith
The Principal Problem With Principal Components Regression, Heidi Margaret Artigue, Gary Smith
Pomona Faculty Publications and Research
Principal components regression (PCR) reduces a large number of explanatory variables down to a small number of principal components. PCR is thought to be more useful, the more numerous the potential explanatory variables. The reality is that a large number of candidate explanatory variables does not make PCR more valuable; instead, it magnifies the failings of PCR.
An Investigation Of The Accuracy Of Parallel Analysis For Determining The Number Of Factors In A Factor Analysis, Mandy Matsumoto
An Investigation Of The Accuracy Of Parallel Analysis For Determining The Number Of Factors In A Factor Analysis, Mandy Matsumoto
Mahurin Honors College Capstone Experience/Thesis Projects
Exploratory factor analysis is an analytic technique used to determine the number of factors in a set of data (usually items on a questionnaire) for which the factor structure has not been previously analyzed. Parallel analysis (PA) is a technique used to determine the number of factors in a factor analysis. There are a number of factors that affect the results of a PA: the choice of the eigenvalue percentile, the strength of the factor loadings, the number of variables, and the sample size of the study. Although PA is the most accurate method to date to determine which factors …
Insights Into Latent Class Analysis, Margaret S. Pepe, Holly Janes
Insights Into Latent Class Analysis, Margaret S. Pepe, Holly Janes
UW Biostatistics Working Paper Series
Latent class analysis is a popular statistical technique for estimating disease prevalence and test sensitivity and specificity. It is used when a gold standard assessment of disease is not available but results of multiple imperfect tests are. We derive analytic expressions for the parameter estimates in terms of the raw data, under the conditional independence assumption. These expressions indicate explicitly how observed two- and three-way associations between test results are used to infer disease prevalence and test operating characteristics. Although reasonable if the conditional independence model holds, the estimators have no basis when it fails. We therefore caution against using …