STATISTICAL METHODS II: TIME SERIES

1970 ◽  
pp. 137-155
Author(s):  
R.E. Munn
Keyword(s):  
Time Series ◽  
Statistical Methods

scholarly journals Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods

2019 ◽  
Vol 3 (2) ◽  
pp. 274-306 ◽  
Author(s):  
Ruben Sanchez-Romero ◽  
Joseph D. Ramsey ◽  
Kun Zhang ◽  
Madelyn R. K. Glymour ◽  
Biwei Huang ◽  
...  
Keyword(s):  
Time Series ◽  
Statistical Methods ◽  
Permutation Test ◽  
Causal Structure ◽  
Multiple Model ◽  
New Methods ◽  
Test Models ◽  
Non Gaussian ◽  
True Structure ◽  
Better Than

We test the adequacies of several proposed and two new statistical methods for recovering the causal structure of systems with feedback from synthetic BOLD time series. We compare an adaptation of the first correct method for recovering cyclic linear systems; Granger causal regression; a multivariate autoregressive model with a permutation test; the Group Iterative Multiple Model Estimation (GIMME) algorithm; the Ramsey et al. non-Gaussian methods; two non-Gaussian methods by Hyvärinen and Smith; a method due to Patel et al.; and the GlobalMIT algorithm. We introduce and also compare two new methods, Fast Adjacency Skewness (FASK) and Two-Step, both of which exploit non-Gaussian features of the BOLD signal. We give theoretical justifications for the latter two algorithms. Our test models include feedback structures with and without direct feedback (2-cycles), excitatory and inhibitory feedback, models using experimentally determined structural connectivities of macaques, and empirical human resting-state and task data. We find that averaged over all of our simulations, including those with 2-cycles, several of these methods have a better than 80% orientation precision (i.e., the probability of a directed edge is in the true structure given that a procedure estimates it to be so) and the two new methods also have better than 80% recall (probability of recovering an orientation in the true structure).

Detecting Determinism in Human Posture Control Data

1998 ◽  
Vol 08 (01) ◽  
pp. 179-188 ◽  
Author(s):  
L. Y. Cao ◽  
B. G. Kim ◽  
J. Kurths ◽  
S. Kim
Keyword(s):  
Time Series ◽  
Statistical Methods ◽  
Nonstationary Time Series ◽  
Posture Control ◽  
Nonlinear Prediction ◽  
Control Data ◽  
Human Posture ◽  
Prediction Techniques

In this paper, determinism in human posture control data is investigated using the approach of nonlinear prediction. We first comment that one should be cautious of using some statistical methods to analyze nonstationary time series. Then we test the predictability of the human posture control data with different prediction techniques, and investigate how nonstationarity and noise affect the prediction results. Different time series are tested, including data from healthy and ill persons, and different predictabilities are found in different time series.

Study on the Application of Data Mining Based on Campus Card Platform

2013 ◽  
Vol 846-847 ◽  
pp. 977-980 ◽  
Author(s):  
Yuan Qian ◽  
Quan Shi
Keyword(s):  
College Students ◽  
Data Mining ◽  
Time Series ◽  
Statistical Methods ◽  
Business Intelligence ◽  
Sql Server ◽  
Policy Makers ◽  
University Policy ◽  
Development Tools ◽  
Intelligence Development

The thesis uses data in the database of campus card platform as the analysis object, combined with statistical methods and data mining technology to analyze the students consumption and the situation of the canteens. We use the Microsoft .NET and SQL Server 2008 business intelligence development tools to mine and analyze these data; know canteens consumption and learn about the business status and the popular shops of the canteen by using the K-means algorithm; analyze and predict students behavior and the situation of the canteen by using time series algorithm. It is convenient to manage the college students, and provide data support for university policy makers and shoppers to make plans.

Threshold models for the analysis of environmental extremes

1984 ◽  
Vol 16 (1) ◽  
pp. 20-20
Author(s):  
Richard L. Smith
Keyword(s):  
Time Series ◽  
Statistical Methods ◽  
Extreme Values ◽  
Threshold Level ◽  
High Threshold ◽  
Threshold Models ◽  
Stationary Sequences ◽  
Environmental Extremes

Statistical methods for analysing the extreme values of a time series may be based on the observed exceedances of the series above a high threshold level. Todorovic (1979) has developed this approach in detail; other relevant references are North (1980) and the English Flood Studies Report (1975). One way of motivating these models is by reference to the theory of extremes in stationary sequences, due to Leadbetter and others.

Change-Points Detections for Interrupted Time Series Analysis: A Literature Review

2013 ◽  
Vol 462-463 ◽  
pp. 187-192
Author(s):  
Jing Bo Chen ◽  
Jun Bao Zheng ◽  
Lei Yang ◽  
Ya Ming Wang
Keyword(s):  
Time Series ◽  
Literature Review ◽  
Time Series Analysis ◽  
Statistical Methods ◽  
Interrupted Time Series ◽  
Change Points ◽  
Detection Methods ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
Common Purpose

General review of Change-Points detection methods applied in Interrupted Time Series Analysis for recent years. Articles from domains like meteorology, hydrology, stock analysis, sequences mining et al. are compared together. The literatures range from the 1980s to 2013. The methods are generally classified in Parametric, Semi-Parametric, and Nonparametric. Some non-statistical methods are also mentioned in this review. Characters of each method are briefly summarized. As all methods mentioned in this review share a common purpose that to detect change-points, most of them can be used in other domains after some proper adjustment.

scholarly journals Comparison of Six Statistical Methods for Interrupted time Series Studies: Empirical Evaluation of 190 Published Series

2020 ◽  
Author(s):  
Simon Turner ◽  
Amalia Karahalios ◽  
Andrew Forbes ◽  
Monica Taljaard ◽  
Jeremy Grimshaw ◽  
...  
Keyword(s):  
Time Series ◽  
Statistical Method ◽  
Statistical Methods ◽  
Statistical Significance ◽  
Empirical Evaluation ◽  
Interrupted Time Series ◽  
Series Data ◽  
Standard Errors ◽  
P Values ◽  
The Impact

Abstract Background The Interrupted Time Series (ITS) is a quasi-experimental design commonly used in public health to evaluate the impact of interventions or exposures. Multiple statistical methods are available to analyse data from ITS studies, but no empirical investigation has examined how the different methods compare when applied to real-world datasets. MethodsA random sample of 200 ITS studies identified in a previous methods review were included. Time series data from each of these studies was sought. Each dataset was re-analysed using six statistical methods. Point and confidence interval estimates for level and slope changes, standard errors, p-values and estimates of autocorrelation were compared between methods. ResultsFrom the 200 ITS studies, including 230 time series, 190 datasets were obtained. We found that the choice of statistical method can importantly affect the level and slope change point estimates, their standard errors, width of confidence intervals and p-values. Statistical significance (categorised at the 5% level) often differed across the pairwise comparisons of methods, ranging from 4% to 25% disagreement. Estimates of autocorrelation differed depending on the method used and the length of the series. ConclusionsThe choice of statistical method in ITS studies can lead to substantially different conclusions about the impact of the interruption. Pre-specification of the statistical method is encouraged, and naive conclusions based on statistical significance should be avoided.

scholarly journals Evaluation of statistical methods used in the analysis of interrupted time series studies: a simulation study

2020 ◽  
Author(s):  
Simon L Turner ◽  
Andrew B Forbes ◽  
Amalia Karahalios ◽  
Monica Taljaard ◽  
Joanne E McKenzie
Keyword(s):  
Time Series ◽  
Statistical Methods ◽  
Simulated Data ◽  
Population Level ◽  
Interrupted Time Series ◽  
Ordinary Least Squares ◽  
In Series ◽  
Time Series Studies ◽  
Unbiased Estimates ◽  
Biased Estimates

AbstractInterrupted time series (ITS) studies are frequently used to evaluate the effects of population-level interventions or exposures. To our knowledge, no studies have compared the performance of different statistical methods for this design. We simulated data to compare the performance of a set of statistical methods under a range of scenarios which included different level and slope changes, varying lengths of series and magnitudes of autocorrelation. We also examined the performance of the Durbin-Watson (DW) test for detecting autocorrelation. All methods yielded unbiased estimates of the level and slope changes over all scenarios. The magnitude of autocorrelation was underestimated by all methods, however, restricted maximum likelihood (REML) yielded the least biased estimates. Underestimation of autocorrelation led to standard errors that were too small and coverage less than the nominal 95%. All methods performed better with longer time series, except for ordinary least squares (OLS) in the presence of autocorrelation and Newey-West for high values of autocorrelation. The DW test for the presence of autocorrelation performed poorly except for long series and large autocorrelation. From the methods evaluated, OLS was the preferred method in series with fewer than 12 points, while in longer series, REML was preferred. The DW test should not be relied upon to detect autocorrelation, except when the series is long. Care is needed when interpreting results from all methods, given confidence intervals will generally be too narrow. Further research is required to develop better performing methods for ITS, especially for short series.

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