Scaling features of intermittent dynamics: Differences of characterizing correlated and anti-correlated data sets

2019 ◽  
Vol 536 ◽  
pp. 122586 ◽  
Author(s):  
O.N. Pavlova ◽  
A.N. Pavlov
Keyword(s):  
Correlated Data ◽  
Data Sets ◽  
Scaling Features

scholarly journals Comparison of SMMR and SSM/I passive microwave data collected over Antarctica

1993 ◽  
Vol 17 ◽  
pp. 131-136 ◽  
Author(s):  
Kenneth C. Jezek ◽  
Carolyn J. Merry ◽  
Don J. Cavalieri
Keyword(s):  
Brightness Temperature ◽  
Microwave Radiometer ◽  
Correlated Data ◽  
Data Sets ◽  
Microwave Brightness Temperature ◽  
Brightness Temperatures ◽  
Scatter Plots ◽  
Microwave Imager ◽  
Highly Correlated ◽  
The Antarctic

Spaceborne data are becoming sufficiently extensive spatially and sufficiently lengthy over time to provide important gauges of global change. There is a potentially long record of microwave brightness temperature from NASA's Scanning Multichannel Microwave Radiometer (SMMR), followed by the Navy's Special Sensor Microwave Imager (SSM/I). Thus it is natural to combine data from successive satellite programs into a single, long record. To do this, we compare brightness temperature data collected during the brief overlap period (7 July-20 August 1987) of SMMR and SSM/I. Only data collected over the Antarctic ice sheet are used to limit spatial and temporal complications associated with the open ocean and sea ice. Linear regressions are computed from scatter plots of complementary pairs of channels from each sensor revealing highly correlated data sets, supporting the argument that there are important relative calibration differences between the two instruments. The calibration scheme was applied to a set of average monthly brightness temperatures for a sector of East Antarctica.

Time Series Control Charts for Correlated and Contaminated Data

1986 ◽  
Vol 108 (3) ◽  
pp. 219-226 ◽  
Author(s):  
B. D. Notohardjono ◽  
D. S. Ermer
Keyword(s):  
Time Series ◽  
Control Charts ◽  
Maximum Principal Stress ◽  
Correlated Data ◽  
Model Parameters ◽  
Data Sets ◽  
Process Change ◽  
Contaminated Data ◽  
Model Residuals ◽  
Control Limits

This paper discusses the development of control charts for correlated and contaminated data. For illustration the charts were applied to a set of maximum principal-stress data at two locations on a blast furnace shell. The Dynamic Data System (DDS) approach was used to model the correlated data which contained several types of discrepancies. After the standard DDS models were found, control charts for the averages and variances of the model residuals were constructed for two data sets. For more effective analysis, two methods for calculating the control limits for both charts are given. With this approach, dynamic process change, such as an increase in the production rate or the wearing out of the sacrificial lining, can be detected and separated from data with collection errors from instrument malfunctions. Furthermore, the tap hole opening timing is identified from the DDS model parameters, to help verify the time series model.

scholarly journals Comparison of SMMR and SSM/I passive microwave data collected over Antarctica

1993 ◽  
Vol 17 ◽  
pp. 131-136 ◽  
Author(s):  
Kenneth C. Jezek ◽  
Carolyn J. Merry ◽  
Don J. Cavalieri
Keyword(s):  
Brightness Temperature ◽  
Microwave Radiometer ◽  
Correlated Data ◽  
Data Sets ◽  
Microwave Brightness Temperature ◽  
Brightness Temperatures ◽  
Scatter Plots ◽  
Microwave Imager ◽  
Highly Correlated ◽  
The Antarctic

Spaceborne data are becoming sufficiently extensive spatially and sufficiently lengthy over time to provide important gauges of global change. There is a potentially long record of microwave brightness temperature from NASA's Scanning Multichannel Microwave Radiometer (SMMR), followed by the Navy's Special Sensor Microwave Imager (SSM/I). Thus it is natural to combine data from successive satellite programs into a single, long record. To do this, we compare brightness temperature data collected during the brief overlap period (7 July-20 August 1987) of SMMR and SSM/I. Only data collected over the Antarctic ice sheet are used to limit spatial and temporal complications associated with the open ocean and sea ice. Linear regressions are computed from scatter plots of complementary pairs of channels from each sensor revealing highly correlated data sets, supporting the argument that there are important relative calibration differences between the two instruments. The calibration scheme was applied to a set of average monthly brightness temperatures for a sector of East Antarctica.

scholarly journals Technical Note: Trend estimation from irregularly sampled, correlated data

2009 ◽  
Vol 9 (6) ◽  
pp. 27675-27692
Author(s):  
T. von Clarmann ◽  
G. Stiller ◽  
U. Grabowski ◽  
J. Orphal
Keyword(s):  
Regression Line ◽  
Technical Note ◽  
Correlated Data ◽  
Data Sets ◽  
State Variable ◽  
Error Covariance ◽  
Data Points ◽  
Atmospheric State ◽  
Global Data Sets ◽  
Global Data

Abstract. Estimation of a trend of an atmospheric state variable is often performed by fitting a linear regression line to a set of data of this variable sampled at different times. Often these data are irregularly sampled in space and time and clustered in a sense that error correlations among data points cause a similar error of data points sampled at similar times. Since this can affect the estimated trend, we suggest to take the full error covariance matrix of the data into account. Superimposed periodic variations can be jointly fitted in a straight forward manner, even if the shape of the periodic function is not known. Global data sets, particularly satellite data, can form the basis to estimate the error correlations.

scholarly journals Scale Features of a Network Echo Mechanism: Case Study for the Different Internet Paths

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Teimuraz Matcharashvili ◽  
Archil Prangishvili ◽  
Zurab Tsveraidze ◽  
Levan Laliashvili
Keyword(s):  
Time Series ◽  
Network Performance ◽  
Fluctuation Analysis ◽  
Data Sets ◽  
Round Trip ◽  
Internet Performance ◽  
The Right ◽  
Scaling Features ◽  
Detrended Fluctuation

We have investigated dynamics of the Internet performance through the assessment of scaling features of a network ICMP echo mechanism or pinging. Time series of round-trip times (RTT) from the host computer to 5 destination hosts and back, recorded during three consecutive days and nights, have been used. To assess correlation and scaling features of network echo mechanism, we used method of detrended fluctuation analysis (DFA) for RTT data sets. It was shown that for different, 10 minute long periods of day and night observations, RTT data sets mostly fluctuate within a narrow range, though sometimes we observe strong sharp spikes. RTT variations mostly reveal persistent behavior. DFA fluctuation curves often are characterized by crossovers indication stronger or lesser changes in the dynamics of network performance. Distribution function of DFA scaling exponents of considered RTT time series mostly was asymmetric with long tail on the right hand side. Dynamical changes occurring in the scaling features of Internet network as assessed by RTT fluctuations do not depend on the location of the host and destination nodes. Larger delays in round-trip time responses make the scaling behavior of the RTT series complicated and strongly influence their long range correlation features.

scholarly journals Quantifying Suspiciousness within correlated data sets

2020 ◽  
Vol 496 (4) ◽  
pp. 4647-4653 ◽  
Author(s):  
Pablo Lemos ◽  
Fabian Köhlinger ◽  
Will Handley ◽  
Benjamin Joachimi ◽  
Lorne Whiteway ◽  
...  
Keyword(s):  
Parameter Space ◽  
Internal Consistency ◽  
Bayesian Method ◽  
Correlated Data ◽  
Data Sets ◽  
Summary Statistics ◽  
Photometric Redshift ◽  
Data Space

ABSTRACT We propose a principled Bayesian method for quantifying tension between correlated data sets with wide uninformative parameter priors. This is achieved by extending the Suspiciousness statistic, which is insensitive to priors. Our method uses global summary statistics, and as such it can be used as a diagnostic for internal consistency. We show how our approach can be combined with methods that use parameter space and data space to identify the existing internal discrepancies. As an example, we use it to test the internal consistency of the KiDS-450 data in four photometric redshift bins, and to recover controlled internal discrepancies in simulated KiDS data. We propose this as a diagnostic of internal consistency for present and future cosmological surveys, and as a tension metric for data sets that have non-negligible correlation, such as Large Synoptic Spectroscopic Survey and Euclid.

scholarly journals Correlation Versus Interchangeability: The Limited Robustness of Empirical Findings on Democracy Using Highly Correlated Data Sets

2003 ◽  
Vol 11 (2) ◽  
pp. 196-203 ◽  
Author(s):  
Gretchen Casper ◽  
Claudiu Tufis
Keyword(s):  
Comparative Politics ◽  
Correlated Data ◽  
Statistical Analyses ◽  
Data Sets ◽  
Time Periods ◽  
Highly Correlated ◽  
Using Data

This article shows that highly correlated measures can produce different results. We identify a democratization model from the literature and test it in more than 120 countries from 1951 to 1992. Then, we check whether the results are robust regarding measures of democracy, time periods, and levels of development. The findings show that measures do matter: Whereas some of the findings are robust, most of them are not. This explains, in part, why the debates on democracy have continued rather than been resolved. More important, it underscores the need for more careful use of measures and further testing to increase confidence in the findings. Scholars in comparative politics are drawn increasingly to large-N statistical analyses, often using data sets collected by others. As in any field, we show how they must be careful in choosing the most appropriate measures for their studies, without assuming that any correlated measure will do.

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