scholarly journals Statistical issues about solar–climate relations

2010 ◽  
Vol 6 (5) ◽  
pp. 565-573 ◽  
Author(s):  
P. Yiou ◽  
E. Bard ◽  
P. Dandin ◽  
B. Legras ◽  
P. Naveau ◽  
...  
Keyword(s):  
Time Series ◽  
Solar Activity ◽  
Geomagnetic Field ◽  
Degrees Of Freedom ◽  
Statistical Significance ◽  
Data Sets ◽  
Statistical Issues ◽  
The Relationship ◽  
Temperature Time ◽  
Field Variability

Abstract. The relationship between solar activity and temperature variation is a frequently discussed issue in climatology. This relationships is usually hypothesized on the basis of statistical analyses of temperature time series and time series related to solar activity. Recent studies (Le Mouël et al., 2008, 2009; Courtillot et al., 2010) focus on the variabilities of temperature and solar activity records to identify their relationships. We discuss the meaning of such analyses and propose a general framework to test the statistical significance for these variability-based analyses. This approach is illustrated using European temperature data sets and geomagnetic field variations. We show that tests for significant correlation between observed temperature variability and geomagnetic field variability is hindered by a low number of degrees of freedom introduced by excessively smoothing the variability-based statistics.

scholarly journals Statistical issues about solar-climate relations

2010 ◽  
Vol 6 (2) ◽  
pp. 461-487 ◽  
Author(s):  
P. Yiou ◽  
E. Bard ◽  
P. Dandin ◽  
B. Legras ◽  
P. Naveau ◽  
...  
Keyword(s):  
Geomagnetic Field ◽  
Degrees Of Freedom ◽  
Statistical Significance ◽  
Random Processes ◽  
Statistical Tools ◽  
Temperature Variations ◽  
Auto Regressive ◽  
Statistical Issues ◽  
The Relationship ◽  
Spurious Results

Abstract. Assessing the relationship between temperature variations and solar activity requires much physical and statistical insight. This paper is devoted to the latter. We focus on the statistical significance of diagnostics to obtain properties of the variability of time series. We illustrate our study by analyses of European temperature datasets and geomagnetic field variations. The goal of the paper is to provide a framework to control the spurious results that statistical tools can generate. We show that some variability diagnostics barely distinguish observed temperatures from auto-regressive random processes. In general, the variability diagnostics between temperature and geomagnetic activity are not significantly correlated, due to a low number of degrees of freedom.

scholarly journals Children with 5′-end NF1 gene mutations are more likely to have glioma

2017 ◽  
Vol 3 (5) ◽  
pp. e192 ◽  
Author(s):  
Corina Anastasaki ◽  
Stephanie M. Morris ◽  
Feng Gao ◽  
David H. Gutmann
Keyword(s):  
Gene Mutation ◽  
Statistical Significance ◽  
Gene Mutations ◽  
Neurofibromatosis Type ◽  
Published Data ◽  
Data Sets ◽  
Nonsense Mutations ◽  
Data Set ◽  
Nf1 Gene ◽  
The Relationship

Objective:To ascertain the relationship between the germline NF1 gene mutation and glioma development in patients with neurofibromatosis type 1 (NF1).Methods:The relationship between the type and location of the germline NF1 mutation and the presence of a glioma was analyzed in 37 participants with NF1 from one institution (Washington University School of Medicine [WUSM]) with a clinical diagnosis of NF1. Odds ratios (ORs) were calculated using both unadjusted and weighted analyses of this data set in combination with 4 previously published data sets.Results:While no statistical significance was observed between the location and type of the NF1 mutation and glioma in the WUSM cohort, power calculations revealed that a sample size of 307 participants would be required to determine the predictive value of the position or type of the NF1 gene mutation. Combining our data set with 4 previously published data sets (n = 310), children with glioma were found to be more likely to harbor 5′-end gene mutations (OR = 2; p = 0.006). Moreover, while not clinically predictive due to insufficient sensitivity and specificity, this association with glioma was stronger for participants with 5′-end truncating (OR = 2.32; p = 0.005) or 5′-end nonsense (OR = 3.93; p = 0.005) mutations relative to those without glioma.Conclusions:Individuals with NF1 and glioma are more likely to harbor nonsense mutations in the 5′ end of the NF1 gene, suggesting that the NF1 mutation may be one predictive factor for glioma in this at-risk population.

On the relationship between global, hemispheric and latitudinal averaged air surface temperature (GISS time series) and solar activity

2012 ◽  
Vol 74 ◽  
pp. 87-93 ◽  
Author(s):  
M.P. Souza Echer ◽  
E. Echer ◽  
N.R. Rigozo ◽  
C.G.M. Brum ◽  
D.J.R. Nordemann ◽  
...  
Keyword(s):  
Time Series ◽  
Solar Activity ◽  
Surface Temperature ◽  
The Relationship

scholarly journals A near-surface sea temperature time series from Trieste, northern Adriatic Sea (1899–2015)

2019 ◽  
Vol 11 (2) ◽  
pp. 761-768 ◽  
Author(s):  
Fabio Raicich ◽  
Renato R. Colucci
Keyword(s):  
Time Series ◽  
Data Sets ◽  
Northern Adriatic Sea ◽  
Sea Temperature ◽  
Northern Adriatic ◽  
Near Surface ◽  
Direct Observations ◽  
Rise Rate ◽  
Temperature Time ◽  
Hourly Basis

Abstract. A time series of near-surface sea temperature was built from observations performed in the harbour of Trieste from 14 July 1899 to 31 December 2015. The description of the observation sites and instruments was possible thanks to historical documents. The measurements consist of two data sets: the first consists of analogue data obtained by means of thermometer and thermograph measurements, recorded one or two times per day, in the periods 1899–1923 and 1934–2008; the second consists of digital records obtained by thermistors on an hourly basis in the period 1986–2015. A quasi-homogeneous time series of daily temperatures at 2 m depth is formed from direct observations at that depth and from temperatures estimated from observations at shallower depths. From this time series a mean temperature rise rate of 1.1±0.3 ∘C per century was estimated, while in 1946–2015 it is 1.3±0.5 ∘C per century. The data are available through SEANOE (https://doi.org/10.17882/58728; Raicich and Colucci, 2019).

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.

scholarly journals SPA SERVICES DISTRIBUTION AND SPECIALIZATION IN KUJAWSKO-POMORSKIE, POMORSKIE AND PODKARPACKIE VOIVODESHIPS COMMUNES

Turyzm ◽  
2017 ◽  
Vol 27 (1) ◽  
pp. 57-63
Author(s):  
Paweł Stelmach
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Functional Relationship ◽  
Data Bank ◽  
Data Sets ◽  
Local Data ◽  
Correlation And Regression Analysis ◽  
Central Statistical ◽  
Central Statistical Office ◽  
The Relationship

Abstract The objective of the article is to identify and explain the relationship between spa services distribution and spa specialization in Kujawsko-Pomorskie, Pomorskie and Podkarpackie Voivodeships spa communes. Correlation and regression analysis were used based on data from the Local Data Bank and unpublished data sets from the Central Statistical Office of Poland. In order to explain the relation between spa services distribution and spa specialization, time-series analysis was used. In five of nine researched communes (Horyniec-Zdrój, Solina, Ustka, Ciechocinek and Inowrocław) there is a functional relationship between spa services distribution and spa specialization.

scholarly journals Standardised Radon Index (SRI): a normalisation of radon data-sets in terms of standard normal variables

2011 ◽  
Vol 11 (7) ◽  
pp. 1839-1844 ◽  
Author(s):  
R. G. M. Crockett ◽  
C. P. Holt
Keyword(s):  
Time Series ◽  
Cross Correlation ◽  
Statistical Significance ◽  
Data Sets ◽  
English Channel ◽  
East Midlands ◽  
Standard Normal ◽  
Precipitation Indices ◽  
The University ◽  
Radon Monitoring

Abstract. During the second half of 2002, from late June to mid December, the University of Northampton Radon Research Group operated two continuous hourly-sampling radon detectors 2.25 km apart in the English East Midlands. This period included the Dudley earthquake (ML = 5, 22 September 2002) and also a smaller earthquake in the English Channel (ML = 3, 26 August 2002). Rolling/sliding windowed cross-correlation of the paired radon time-series revealed periods of simultaneous similar radon anomalies which occurred at the time of these earthquakes but at no other times during the overall radon monitoring period. Standardising the radon data in terms of probability of magnitude, analogous to the Standardised Precipitation Indices (SPIs) used in drought modelling, which effectively equalises different non-linear responses, reveals that the dissimilar relative magnitudes of the anomalies are in fact closely equiprobabilistic. Such methods could help in identifying anomalous signals in radon – and other – time-series and in evaluating their statistical significance in terms of earthquake precursory behaviour.

scholarly journals Spa service distribution and specialisation in kujawsko-pomorskie, pomorskie and podkarpackie voivodship communes

Turyzm ◽  
2017 ◽  
Vol 27 (1) ◽  
pp. 57-63
Author(s):  
Paweł Stelmach
Keyword(s):  
Time Series ◽  
Functional Relationship ◽  
Data Bank ◽  
Data Sets ◽  
Local Data ◽  
Correlation And Regression Analysis ◽  
Service Distribution ◽  
Central Statistical ◽  
Central Statistical Office ◽  
The Relationship

The objective of the article is to identify and explain the relationship between spa services distribution and spa specialization in Kujawsko-Pomorskie, Pomorskie and Podkarpackie Voivodeships spa communes. Correlation and regression analysis were used based on data from the Local Data Bank and unpublished data sets from the Central Statistical Office of Poland. In order to explain the relation between spa services distribution and spa specialization, time-series analysis was used. In five of nine researched communes (Horyniec-Zdrój, Solina, Ustka, Ciechocinek and Inowrocław) there is a functional relationship between spa services distribution and spa specialization.

scholarly journals Bias in community-weighted mean analysis of plant functional traits and species indicator values

2016 ◽  
Author(s):  
David Zeleny
Keyword(s):  
Beta Diversity ◽  
Degrees Of Freedom ◽  
Permutation Test ◽  
Effect Sizes ◽  
Data Sets ◽  
Indicator Values ◽  
Weighted Mean ◽  
Community Weighted Mean ◽  
Species Indicator Values ◽  
The Relationship

One way to analyze the relationship between species attributes and sample attributes via the matrix of species composition is to calculate the community-weighted mean of species attributes (CWM) and relate it to sample attributes by correlation, regression or ANOVA. This weighted-mean approach is frequently used by vegetation ecologists to relate species attributes like plant functional traits or Ellenberg-like species indicator values to sample attributes like measured environmental variables, biotic properties, species richness or sample scores in ordination analysis. The problem with the weighted-mean approach is that, in certain cases, it yields biased results in terms of both effect size and P-values, and this bias is contingent upon the beta diversity of the species composition data. The reason is that CWM values calculated from samples of communities sharing some species are not independent of each other. This influences the number of effective degrees of freedom, which is usually lower than the actual number of samples, and the difference further increases with decreasing beta diversity of the data set. The discrepancy between the number of effective degrees of freedom and the number of samples in analysis turns into biased effect sizes and an inflated Type I error rate in those cases where the significance of the relationship is tested by standard tests, a problem which is analogous to analysis of two spatially autocorrelated variables. Consequently, results of studies using rather homogeneous (although not necessarily small) compositional data sets may be overly optimistic, and effect sizes of studies based on data sets differing by their beta diversity are not directly comparable. Here, I introduce guidelines on how to decide in which situation the bias is actually a problem when interpreting results, recognizing that there are several types of species and sample attributes with different properties and that ecological hypotheses commonly tested by the weighted-mean approach fall into one of three broad categories. I also compare available analytical solutions accounting for the bias (modified permutation test and sequential permutation test using the fourth-corner statistic) and suggest rules for their use.

Physiological time-series analysis: what does regularity quantify?

1994 ◽  
Vol 266 (4) ◽  
pp. H1643-H1656 ◽  
Author(s):  
S. M. Pincus ◽  
A. L. Goldberger
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Approximate Entropy ◽  
Series Data ◽  
Data Sets ◽  
Heart Rate Data ◽  
Mathematical Properties ◽  
Relationship Of ◽  
Algorithm Implementation ◽  
The Relationship

Approximate entropy (ApEn) is a recently developed statistic quantifying regularity and complexity that appears to have potential application to a wide variety of physiological and clinical time-series data. The focus here is to provide a better understanding of ApEn to facilitate its proper utilization, application, and interpretation. After giving the formal mathematical description of ApEn, we provide a multistep description of the algorithm as applied to two contrasting clinical heart rate data sets. We discuss algorithm implementation and interpretation and introduce a general mathematical hypothesis of the dynamics of a wide class of diseases, indicating the utility of ApEn to test this hypothesis. We indicate the relationship of ApEn to variability measures, the Fourier spectrum, and algorithms motivated by study of chaotic dynamics. We discuss further mathematical properties of ApEn, including the choice of input parameters, statistical issues, and modeling considerations, and we conclude with a section on caveats to ensure correct ApEn utilization.

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