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JAQM Volume 15, Issue 1 - June 30, 2020
Contents
Monitoring, Forecasting and Optimization Technique When Multi-variables Shift with Time
Hong MAO
In this paper, the quality control technique and the optimization of adjustment interval of one dimensional quality characteristic are extended to multi-dimensional case in which the vector of the quality characteristics or important financial indices of firms shifts with time. A special multivariate triangle control chart is proposed to control such kind of process. An application is illustrated to monitor and predict the soundness of insurers of U.S. Finally, 7 important issues for further study in future are presented.
Correlation-Comparison Analysis as a new way of Data-Mining: Application to Neural Data
Ivan GRBATINIĆ, Bojana KRSTONOŠIĆ, Dragana SREBRO, Nemanja PURIĆ, Marija DUBAK, Vladan DUŠANIĆ, Vladimir KOSTIĆ, Nebojša MILOŠEVIĆ
This paper tends to present a way of multidimensional data-mining termed correlation-comparison analysis (CCA). It was applied to neural data to show its utility in neuron-classification problem. The CCA represents a semi quantitative way of inter-sample comparisons. The methodology comprises the generation of inter-parametric correlation and alpha-error matrices. The main step is p-comparison for the same parametric pair defined between the two samples. This comparison has a semi-quantitative binary character that does not involve issues like false discovery rate (FDR) in multiple comparisons. As a result, the outcomes obtained are: 1) correlation match, 2) correlation mismatch of the first kind, the main type of the correlation mismatch, 3) correlation mismatch of the second kind, the strongest one but very rarely observed in biological systems and obtained on a very small number of parameters. The correlation mismatch of the first kind is the target mismatch, i.e. the mismatch of tracing interest and represents the reason why the study itself is performed. CCA application led to effective neuromorphofunctional classification of caudate interneurons into appropriate clusters and their feature-based description. CCA analysis is a very useful multidimensional bi-sampled classification tool that can be very useful for similar samples to explain their differences.
Seasonality in the number of hours worked by employed persons – an analysis by activity level
Georgiana Andreea FERARIU, Andreea MIRICA, Marian NECULA, Nicoleta Violeta VELIŞCĂ
The paper tests for time series seasonality in the numbers of hours worked by Romanian employed persons, by activity level. The presence of the seasonality component in a time series can seriously affect interpretation and forecasting of economic phenomena. Detecting and extracting the seasonal component from a time series which displays seasonal pattern is, arguably, an useful econometric technique, especially when time series analysis is used as decision support mechanism.