Shao, 2011 - Google Patents
A simple test of changes in mean in the possible presence of long‐range dependenceShao, 2011
View PDF- Document ID
- 12698054116997693298
- Author
- Shao X
- Publication year
- Publication venue
- Journal of Time Series Analysis
External Links
Snippet
We propose a simple testing procedure to test for a change point in the mean of a possibly long‐range dependent time series. Under the null hypothesis, the series is stationary with long‐range dependence and our test statistic converges to a non‐degenerate distribution …
- 230000001419 dependent 0 abstract description 15
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/06—Investment, e.g. financial instruments, portfolio management or fund management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
- G06Q10/063—Operations research or analysis
- G06Q10/0639—Performance analysis
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
- G06F21/57—Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
- G06F21/577—Assessing vulnerabilities and evaluating computer system security
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/02—Banking, e.g. interest calculation, credit approval, mortgages, home banking or on-line banking
- G06Q40/025—Credit processing or loan processing, e.g. risk analysis for mortgages
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Shao | A simple test of changes in mean in the possible presence of long‐range dependence | |
Szolgayova et al. | Factors influencing long range dependence in streamflow of European rivers | |
Salisu et al. | Stock‐induced Google trends and the predictability of sectoral stock returns | |
Meyer | Constrained penalized splines | |
Mirakbari et al. | Regional bivariate frequency analysis of meteorological droughts | |
Volpi | On return period and probability of failure in hydrology | |
Guure et al. | Bayesian Estimation of Two‐Parameter Weibull Distribution Using Extension of Jeffreys′ Prior Information with Three Loss Functions | |
Farrell et al. | Measuring the financial cycle in South Africa | |
Finger | Revisiting the evaluation of robust regression techniques for crop yield data detrending | |
Kim et al. | Inflation and inflation volatility revisited | |
Li et al. | Local Whittle estimation of long‐range dependence for functional time series | |
Rodrigues et al. | Correlation analysis in contaminated data by singular spectrum analysis | |
Gorus et al. | The relationship between oil prices, oil imports and income level in Turkey: Evidence from Fourier approximation | |
Abujiya et al. | Increasing the sensitivity of cumulative sum charts for location | |
Niazkar et al. | Application of MGGP, ANN, MHBMO, GRG, and linear regression for developing daily sediment rating curves | |
Neumeyer | Smooth residual bootstrap for empirical processes of non‐parametric regression residuals | |
Wagner et al. | Consistent monitoring of cointegrating relationships: The US housing market and the subprime crisis | |
Plakandaras et al. | The informational content of the term spread in forecasting the us inflation rate: a nonlinear approach | |
Chang et al. | Does the magnitude of the effect of inflation uncertainty on output growth depend on the level of inflation? | |
Winship et al. | Expected future performance of salmon abundance forecast models with varying complexity | |
Guo et al. | The design of the S 2 control charts based on conditional performance via exact methods | |
Wang et al. | Distributional change of monthly precipitation due to climate change: comprehensive examination of dataset in southeastern United States | |
Zheng | Testing heteroscedasticity in nonlinear and nonparametric regressions | |
Kabaila et al. | Improved prediction limits for AR (p) and ARCH (p) processes | |
Alshqaq et al. | Some new robust estimators for circular logistic regression model with applications on meteorological and ecological data |