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There are various classically established trend identification tests in the literature and their preliminary explanations are useful for further and innovative trend proposal understandings. In general these methodologies are divided into two groups as parametric and non-parametric approaches. Each group is explained with its proper assumptions, restrictions and mathematical formulations so as to give the reader appreciation of the fundamental concepts, which are useful in the assessment of any trend identification procedure. The regression analysis, which is the first main methodology for the description of the mathematical expression of any trend, is presented with a set of restrictive assumption exposition that are not taken into consideration in many publications throughout the world. It is recommended that in the application of any methodology the researcher should be aware of the assumptions, restrictions and difficulties that may be confronted in the trend identification application researches.
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Chernoff, H., & Zacks, S. (1964). Estimating the current mean of a normal distribution which is subjected to changes in time. Annals of Mathematical Statistics, 35, 999–1018. CrossRef
Connover, W. J. (1971). Practical non-parametric statistics. New York: John Wiley and Sons.
Craddock, J. M. (1979). Methods of comparing annual rainfall records for climatic purposes. Weather, 34, 332–346. CrossRef
Dickey D. A. (1991). A primer on co-integration with an application to money and income, Federal Reserve Bank of St. Louis, 58–78, Reprinted in Rao (1995).
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74, 427–431.
Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49, 1057–1072. CrossRef
Dickey, D. A., Dennis, W., Daniel, J., & Thornton, L. (1991). A primer on cointegration with an application to money and income, Federal Reserve Bank of St. Louis, 58–78, reprinted in Rao (1995).
EPA. (1974). Guideline for the evaluation of air quality trends. Office of air quality planning and standards, U.S. Environmental Protection Agency.
Feller, W. (1968). An introduction to probability theory and its applications (3rd ed., Vol. I), John Wiley and Sons Co.
Gardner, L. A., Jr. (1969). On detecting changes in the mean of normal variates. Annals of Mathematical Statistics, 40, 116–126. CrossRef
Gilbert, R. O. (1987). Statistical methods for environmental pollution monitoring. New York: Van Nostrand Reinhold.
Gomide, F. L. S. (1978). Markovian inputs and the Hurst phenomenon. Journal of Hydrology, 37, 23–45. CrossRef
Helsel, D. R., & Hirsch, R. M. (1992). Statistical methods in water resources. Studies in environmental science 49. New York: Elsevier. (Available on-line as a pdf file at: http://water.usgs.gov/pubs/twri/twri4a3/) (Retrieved September 15, 2011).
Himmelblau, D. M. (1969). Process analysis by statistical methods. New York: John Wiley and Sons.
Hurst, H. E. (1951). Long-term storage capacity of reservoirs. Transactions of the American Society of Civil Engineers, 116, 70–808.
Hurst, H. E. (1956). Methods of using long-term storage in reservoirs. Proceedings of the Institution of Civil Engineers, Part I, 519–542.
Kanji, G. K. (2001). 100 statistical tests. New Delhi: Sage Publication, 111”pp.
Kendall, M. G. (1955). Rank correlation methods, Charles Griffin, London.
Kendall, M. G. (1973). Time series. London: Griffin.
Kendall, M. G. (1975). Rank correlation methods, 4th edition. Charles Griffin, London, U.K.
Kendall, M. G., & Stuart, A. (1952). The advanced theory of statistics. Vol. 1: Distribution theory. Griffin, London.
Kendall, M. G., & Stuart, A. (1973). The advanced theory of statistics (Vol. II). New York: Hafner.
Khaliq, M. N., Ouarda, T. B. M. J., Gachon, P., Sushama, L., & StHilaire, A. (2009). Identification of hydrological trends in the presence of serial and cross correlations: A review of selected methods and their application to annual flow regimes of Canadian rivers. Journal of Hydrology, 368, 117–130.
Koçak, K., & Şen, Z. (1998). Applied examination of dry and wet day occurrences via markov chain approach. Turkish Journal of Engineering and Environmental Science, 22, 479–487.
Kolaz, D. J., & Swinford, R. L. (1988). Ozone air quality: How does chicago rate? 81st annual meeting of the air pollution control association. Texas, USA: Dallas.
Kolaz, D. J., & Swinford, R. L. (1989). Ozone trends in the greater chicago area. Ozone Conference on Federal Controls for Ozone Around Lake Michigan, Lake Michigan States’ Section and Wisconsin Chapter of the Air and Waste Management Association.
Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54, 159–178 (North-Holland).
Lettenmaier, D. P. (1976). Detection of trends in water quality data from records with dependent observations. Water Resources Research, 12(5), 1037–1046. CrossRef
Mann, H. B. (1945). Nonparametric tests against trend. Econometrica, 13(3), 245–259. CrossRef
Mills, T. C. (1993). The econometric modelling of financial time series. Cambridge: Cambridge University Press.
Montgomery, R. H., & Reckhow, K. H. (1984). Techniques for detecting trends in lake water quality. Water Resources Bulletin, 20(1), 43–52. CrossRef
Parzen, E. (1960). Modern probability theory and its applications. John Wiley & Sons, New York.
Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75, 335–346.
Qwen, D. B. (1962). Handbook of srtatistical tables. Reading, Massachusetts, USA: Addison-Wesley.
Rao, B. B. (1995). Cointegration for the applied economist. London: Macmillan.
Saldarriga, J., & Yevjevich, V. (1979). Application of run-lengths to hydrologic time series. Hydrology Paper 40. Colorado State University, Fort Collins, USA.
Searcy, J. K., & Hardison, H. H. (1960). Manual of hydrology: Part 1, general surface-water techniques double-mass curves. U.S. Geological Survey, Water Supply Paper 1541-B.
Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall’s Tau. Journal of the American Statistical Association, 63(324), 1379–1389. CrossRef
Şen, Z. (1974). Small sample properties of stationary stochastic processes and hurst phenomenon in hydrology. Unpublished Ph. D. Thesis, University of London, 256 pp.
Şen, Z. (1976). Wet and dry periods of annual flow series. Journal of the Hydraulics Division, 102, 1503–1514 (ASCE Proceedings of the Paper, 12497).
Şen, Z. (1978). Autorun analysis of hydrologic time series. Journal of Hydrology, 36, 75–85. CrossRef
Shahin, et al. (1993). In D. Machiwal & M. K. Jha. In hydrologic time series analysis: Theory and practice (p. 301). Springer Publisher.
Sneyer, R. (1992). On the use of statistical analysis for the objective determination of climate change. Meteorologishe Zeitshrifft, N.F., 247–256.
Sonali, P. & Kumar, D. N. (2013). Review of trend detection methods and their application to detect temperature changes in India. Journal of Hydrology, 476, 212–227.
Sweitzer, T. A., & Kolaz, D. J. (1984). An assessment of the influence of meteorology on the trend of ozone concentrations in the chicago area. In Air Pollution Control Association Specialty Conference on “Quality Assurance in Air Pollution Measurements,” Boulder, Colorado, USA.
Syczewska, E. M., (2010). Empirical power of the Kwiatkowski-Phillips-Schmidt-Shin test. Working Paper No. 3–10, Department of Applied Econometrics website at: http://www.sgh.waw.pl/instytuty/zes/wp/.
Wallis, J. R., & O’Connell, P. E. (1973). Firm reservoir yield—How reliable are historic hydrological records? Hydrological Sciences Bulletin, 18, 347–365. CrossRef
WMO (World Meteorological Organization). (1966). Climate change. Technical Note No. 79, WMO No. 195, TP 200, I-20, WM, Geneva, Switzerland.
Yevjevich, V. (1967). An objective approach to definition and investigation of continental hydrologic droughts. Hydrology Paper 23. Colorado State University, Fort Collins, USA.
Yevjevich, V., & Jeng, R.J. (1969). Properties of non-homogeneous hydrologic series. Hydrolology Paper 32, Colo. State Univ., Fort Collins, USA.
Yue, S., & Wang, C. (2004). The Mann–Kendall test modified by effective sample size to detect trend in serially correlated hydrological series. Water Resource Manage, 18, 201–218.
Yue, S., Pilon, P., & Phinney, B. (2004). Canadian streamflow trend detection: impacts of serial and cross-correlation. Hydrogical Sciences Journal, 48(1), 51-64.
- Statistical Trend Tests
- Chapter 3
Systemische Notwendigkeit zur Weiterentwicklung von Hybridnetzen