scholarly journals Improved Nonparametric Confidence Intervals in Time Series Regressions

2003 ◽  
Author(s):  
Joseph P. Romano ◽  
Michael NMI Wolf
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Nonparametric Confidence Intervals

Nonparametric confidence intervals for location in time series data

2018 ◽  
Vol 48 (5) ◽  
pp. 1292-1311
Author(s):  
A. Corkum ◽  
P. Cabilio ◽  
Y. Zhang
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Time Series Data ◽  
Series Data ◽  
Nonparametric Confidence Intervals

scholarly journals Improved nonparametric confidence intervals in time series regressions

2006 ◽  
Vol 18 (2) ◽  
pp. 199-214 ◽  
Author(s):  
Joseph P. Romano ◽  
Michael Wolf
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Nonparametric Confidence Intervals

scholarly journals An ARFIMA-based model for daily precipitation amounts with direct access to fluctuations

2020 ◽  
Vol 34 (10) ◽  
pp. 1487-1505
Author(s):  
Katja Polotzek ◽  
Holger Kantz
Keyword(s):  
Time Series ◽  
Sample Size ◽  
Confidence Intervals ◽  
Daily Precipitation ◽  
Rainfall Simulation ◽  
Direct Access ◽  
Effective Sample Size ◽  
Sample Mean ◽  
Daily Data ◽  
Synthetic Time Series

Abstract Correlations in models for daily precipitation are often generated by elaborate numerics that employ a high number of hidden parameters. We propose a parsimonious and parametric stochastic model for European mid-latitude daily precipitation amounts with focus on the influence of correlations on the statistics. Our method is meta-Gaussian by applying a truncated-Gaussian-power (tGp) transformation to a Gaussian ARFIMA model. The speciality of this approach is that ARFIMA(1, d, 0) processes provide synthetic time series with long- (LRC), meaning the sum of all autocorrelations is infinite, and short-range (SRC) correlations by only one parameter each. Our model requires the fit of only five parameters overall that have a clear interpretation. For model time series of finite length we deduce an effective sample size for the sample mean, whose variance is increased due to correlations. For example the statistical uncertainty of the mean daily amount of 103 years of daily records at the Fichtelberg mountain in Germany equals the one of about 14 years of independent daily data. Our effective sample size approach also yields theoretical confidence intervals for annual total amounts and allows for proper model validation in terms of the empirical mean and fluctuations of annual totals. We evaluate probability plots for the daily amounts, confidence intervals based on the effective sample size for the daily mean and annual totals, and the Mahalanobis distance for the annual maxima distribution. For reproducing annual maxima the way of fitting the marginal distribution is more crucial than the presence of correlations, which is the other way round for annual totals. Our alternative to rainfall simulation proves capable of modeling daily precipitation amounts as the statistics of a random selection of 20 data sets is well reproduced.

Time series characteristics and the widths of judgemental confidence intervals

1992 ◽  
Vol 7 (4) ◽  
pp. 413-420 ◽  
Author(s):  
Marcus O'Connor ◽  
Michael Lawrence
Keyword(s):  
Time Series ◽  
Confidence Intervals

scholarly journals Brief communication "Improving the actual coverage of subsampling confidence intervals in atmospheric time series analysis"

2012 ◽  
Vol 19 (5) ◽  
pp. 473-477
Author(s):  
A. Gluhovsky ◽  
T. Nielsen
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Confidence Intervals ◽  
Coverage Probability ◽  
Resampling Methods ◽  
Large Sample ◽  
Series Analysis

Abstract. In atmospheric time series analysis, where only one record is typically available, subsampling (which works under the weakest assumptions among resampling methods), is especially useful. In particular, it yields large-sample confidence intervals of asymptotically correct coverage probability. Atmospheric records, however, are often not long enough, causing a substandard coverage of subsampling confidence intervals. In the paper, the subsampling methodology is extended to become more applicable in such practically important cases.

Fuzzy Autocorrelation Model with Confidence Intervals of Fuzzy Random Data

2014 ◽  
Vol 18 (2) ◽  
pp. 197-203 ◽  
Author(s):  
Yoshiyuki Yabuuchi ◽  
◽  
Junzo Watada ◽  
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Fuzzy Time Series ◽  
Economic Systems ◽  
Random Data ◽  
Human Behaviors ◽  
Section Data ◽  
Tick Data ◽  
Economic Analyses ◽  
Fuzzy Random

Economic analyses are typical methods based on timeseries data or cross-section data. Economic systems are complex because they involve human behaviors and are affected by many factors. When a system includes such uncertainty, as those concerning human behaviors, a fuzzy system approach plays a pivotal role in such analysis. In this paper, we propose a fuzzy autocorrelation model with confidence intervals of fuzzy random timeseries data. These confidence intervals play an essential role in dealing with fuzzy random data on the fuzzy autocorrelation model that we have presented. We analyze tick-by-tick data of stock transactions and compare two time-series models, a fuzzy autocorrelation model proposed by us, and a new fuzzy time-series model that we propose in this paper.

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