Intraindividual reference values

2004 ◽  
Vol 42 (7) ◽  
Author(s):  
Josep M. Queraltó
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Statistical Significance ◽  
Reference Intervals ◽  
First Order ◽  
Series Analysis ◽  
Statistical Framework ◽  
Reliable Model ◽  
Key Concepts ◽  
High Degree

AbstractWhen a biological quantity examination exhibits a high degree of individuality, developing a strategy for interpreting these values in an individual context can be a useful alternative. Time-series analysis is the appropriate statistical framework to build a model for explanation of the behaviour of laboratory information and to forecast future values. The key concepts in this approach are autocorrelation and withinperson variance. Unfortunately, the powerful tools provided by time-series analysis require many observations, a requisite difficult to meet in every day practice. However, introducing some restrictions in the autocorrelation parameter of the most reliable model, the first order autocorrelation model, and using the average within-person variance from a selected population, it is possible to build predictive reference intervals for an individual, based on only few observations. The most common case is the minimum time series: when there are just two observations. The statistical significance of the change from a previous observation is a problem that arises from both quality control (delta checks) and the interpretative diagnostic fields (reference change limit). Applying the same restrictive criteria, it is possible to develop specific limits for a difference between consecutive observations based on a within-person variance selected from the distribution of variances found in a sample of similar individuals.

scholarly journals Wavelet Cross-correlation in Bivariate Time-Series Analysis

2018 ◽  
Vol 19 (3) ◽  
pp. 391
Author(s):  
Eniuce Menezes de Souza ◽  
Vinícius Basseto Félix
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Cross Correlation ◽  
Multiple Scales ◽  
Statistical Significance ◽  
Dependent Data ◽  
Data Sets ◽  
Translation Invariant ◽  
Series Analysis ◽  
Bivariate Time Series

The estimation of the correlation between independent data sets using classical estimators, such as the Pearson coefficient, is well established in the literature. However, such estimators are inadequate for analyzing the correlation among dependent data. There are several types of dependence, the most common being the serial (temporal) and spatial dependence, which are inherent to the data sets from several fields. Using a bivariate time-series analysis, the relation between two series can be assessed. Further, as one time series may be related to an other with a time offset (either to the past or to the future), it is essential to also consider lagged correlations. The cross-correlation function (CCF), which assumes that each series has a normal distribution and is not autocorrelated, is used frequently. However, even when a time series is normally distributed, the autocorrelation is still inherent to one or both time series, compromising the estimates obtained using the CCF and their interpretations. To address this issue, analysis using the wavelet cross-correlation (WCC) has been proposed. WCC is based on the non-decimated wavelet transform (NDWT), which is translation invariant and decomposes dependent data into multiple scales, each representing the behavior of a different frequency band. To demonstrate the applicability of this method, we analyze simulated and real time series from different stochastic processes. The results demonstrated that analyses based on the CCF can be misleading; however, WCC can be used to correctly identify the correlation on each scale. Furthermore, the confidence interval (CI) for the results of the WCC analysis was estimated to determine the statistical significance.

Fluctuations in Schizophrenic Births by Year

1996 ◽  
Vol 169 (6) ◽  
pp. 772-775 ◽  
Author(s):  
E. Fuller Torrey ◽  
Robert R. Rawlings
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Temperature Variation ◽  
Statistical Approach ◽  
Statistical Significance ◽  
Severe Weather ◽  
Series Analysis ◽  
Birth Data

BackgroundIt has previously been reported that births of individuals who later develop schizophrenia vary by birth year.MethodBirth data were analysed on 34 024 individuals diagnosed with DSM–III–R disorganised, catatonic, and undifferentiated schizophrenia using time series analysis.ResultsMinor yearly fluctuations were observed but did not achieve statistical significance. Thus earlier findings reported in the literature could not be replicated using a different statistical approach.ConclusionThe findings do not support theories which assume major yearly fluctuations in the births of individuals with schizophrenia. Such theories include the perinatal effects of influenza, temperature variation, and the effects of severe weather.

scholarly journals Groundwater Circulation in Fractured and Karstic Aquifers of the Umbria-Marche Apennine

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1039 ◽  
Author(s):  
Andrea Tamburini ◽  
Marco Menichetti
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Fracture Network ◽  
Series Analysis ◽  
Drainage Time ◽  
Groundwater Circulation ◽  
Freshwater Resource ◽  
Recession Analysis ◽  
Conduit Networks ◽  
High Degree

The Umbria-Marche Apennine has a large number of springs that drain water stored in carbonate formations. Spring groundwater constitutes a crucial freshwater resource for many countries, regions, and cities around the world. This study aimed to understand the hydrological mechanisms behind groundwater circulation and their relationship to the structural and stratigraphic settings of specific aquifers. Recession analysis and time series analysis were applied to the daily discharge of six springs monitored over eight years. Both analyses indicated the presence of two types of aquifers: aquifer with unimodal behavior and aquifer with bimodal behavior. The first are characterized by two hydrodynamic sub-regimes, in which fracture networks control the baseflow and conduit networks control the quickflow. In contrast, other springs present only one hydrodynamic sub-regime related to fracture network drainage. Time series analysis confirms the results of recession analysis, showing a large memory effect and a large response time, implying the dominance of the baseflow sub-regime. These results indicate that the Maiolica Formation is characterized by a high degree of fracturation and slight karstification, which control infiltration and percolation, whereas the Calcare Massiccio Formation regulates groundwater circulation in the deeper zones of the aquifer, characterized by a high degree of karstification through moderately developed conduit networks.

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