Advertisement

Weitere Prüfverfahren

  • Lothar Sachs
Chapter
  • 503 Downloads

Zusammenfassung

Wenn n Personen einen Sonnenbrand haben und zwei bewährte Behandlungsmöglichkeiten verfügbar sind, wird jede Person, die keine Behandlungserfahrung hat, gut beraten sein, beide Behandlungen an vergleichbar geschädigten und symmetrisch zueinander liegenden Hautpartien anzuwenden: es liegen dann blockinterne Vergleiche vor.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Literatur zur Entdeckung von Lageänderungen

  1. Chatfield, C. (1982): Analyse von Zeitreihen. Eine Einführung. Übers. aus d. Engl. (2nd ed. 1980) durch H. Grimm. (Teubner; 239 S.) Leipzig [vgl. auch R. Schlittgen (1990): Robuste Glättung von Zeitreihen. Allgemeines Statistisches Archiv 74, 223–250 u. 75 (1991), 1–131]Google Scholar
  2. Chatfield, C. (1988): What is the “best” method of forecasting? Journal of Applied Statistics 15, 19–38CrossRefGoogle Scholar
  3. Chatfield, C. (1989): The Analysis of Time Series. An Introduction. 4th ed. (Chapman and Hall; pp. 241) London (5th ed. 1996, pp. 283)Google Scholar
  4. Chatfield, C.: (1995 a): Problem Solving. A Statistician’s Guide. 2nd ed. (Chapman and Hall; pp. 325) London and New YorkGoogle Scholar
  5. Chatfield, C. (1995b): Model uncertaintly, data mining and statistical inference. With discussion. Journal of the Royal Statistical Society A 158, 419–466CrossRefGoogle Scholar
  6. Cook, T.D., and Campbell, D.T. (1979): Quasi-Experimentation. Design and Analysis Issues for Field Settings. (Rand McNally College Publ. Comp.; pp. 405) ChicagoGoogle Scholar
  7. Diggle, P.J. (1990): Time Series. A Biostatistical Introduction. (Clarendon Press; pp. 257) OxfordGoogle Scholar
  8. Engeman, R., and Swanson, G.D. (1990): Alternatives to Fisher’s “exact test” for analyzing 2 × 2 tables with small cell sizes. With discussion. Biometrics 46, 267–269 [vgl. auch 44 (1988), 1–22 und 48 (1992), 1103–1112, Journal of the Royal Statistical Society A155 (1992), 395–402, The Canadian Journal of Statistics 20 (1992), 201–209]Google Scholar
  9. Metzler, P., and Nickel, B. (1986): Zeitreihen- und Verlaufsanalyse. (Hirzel; 248 S.) LeipzigGoogle Scholar
  10. Nesselroade, J.R., and Baltes, P.B. (Eds.; 1979 ): Longitudinal Research in the Study of Behavior and Development. (Academic Press; pp. 386) New York, London, Toronto [vgl. auch Psychological Bulletin 88 (1980), 622–637]Google Scholar
  11. Newbold, P. (1988): Some recent developments in time series analysis-III. International Statistical Review 56, 17–29MathSciNetzbMATHCrossRefGoogle Scholar
  12. Ostrom, Ch.W., Jr. (1990): Time Series Analysis. Regression Techniques. 2nd ed. (Sage Publ. Series 07–009; pp. 95) Beverly Hills and LondonGoogle Scholar
  13. Pinnekamp, H.-J., and Siegmann, F. (1988): Deskriptive Statistik. Einführung in die statistische Methodenlehre. (R. Oldenbourg; 266 S.) München und WienGoogle Scholar
  14. Schmitz, B. (1989): Einführung in die Zeitreihenanalyse. Modelle, Softwarebeschreibung, Anwendungen. Methoden der Psychologie, Bd. 10 (Verlag H. Huber; 235 S.) Bern, Stuttgart, TorontoGoogle Scholar
  15. Tiede, M. (1987): Statistik. Regressions-and Korrelationsanalyse. (R. Oldenbourg; 455 S.) München und WienGoogle Scholar
  16. Mann, H.B., and Whitney, D.R.: On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Statist. 18 (1947), 50–60MathSciNetzbMATHCrossRefGoogle Scholar
  17. Maxwell, S.E., and Delaney, H.D. (1990): Designing Experiments and Analyzing Data: A Model Comparison Perspective. (Wadsworth; pp. 902 ) Belmont, Calif.zbMATHGoogle Scholar
  18. Michels, P. (1992): Nichtparametrische Analyse und Prognose von Zeitreihen. (Springer; 234 S.) Berlin, Heidelberg, New YorkzbMATHCrossRefGoogle Scholar
  19. Petermann, F. (Hrsg.; 1989 ): Einzelfallanalyse. 2. völlig überarb. Aufl. (R. Oldenbourg; 318 S.) München und WienGoogle Scholar
  20. Polasek, W. (1994): Explorative Daten-Analyse. EDA. Einführung in die deskriptive Statistik. 2. Aufl. (Springer; 345 S.) Berlin, Heidelberg, New YorkGoogle Scholar
  21. Strecker, H. (1987): Statistische Erhebungen: Methoden und Ergebnisse. Ausgewählte Schriften. (Vandenhoeck und Ruprecht; 341 S.) Göttingen [vgl. auch ifo Studien 41 (1995), 641–652 und Allgemeines Statistisches Archiv 79 (1995), 402–424]Google Scholar
  22. von Eye, A. (Ed.; 1990 ): Statistical Methods in Longitudinal Research. Vol. I: Principles and Structuring Change; Vol. II: Time Series and Categorical Longitudinal Data. (Academic Press; pp. 570 + 17 ) San Diego, Calif.Google Scholar
  23. Chatfield, C. (1982): Analyse von Zeitreihen. Eine Einführung. Übers. aus d. Engl. (2nd ed. 1980) durch H. Grimm. (Teubner; 239 S.) Leipzig [vgl. auch R. Schlittgen (1990): Robuste Glättung von Zeitreihen. Allgemeines Statistisches Archiv 74, 223–250 u. 75 (1991), 1–131]Google Scholar
  24. Chatfield, C. (1988): What is the “best” method of forecasting? Journal of Applied Statistics 15, 19–38CrossRefGoogle Scholar
  25. Fox, J., and Long, J.S. (Eds.; 1990 ): Modern Methods of Data Analysis. (Sage; pp. 416) Beverly Hills and LondonGoogle Scholar
  26. Hartung, J., und Elpelt, Bärbel (1989): Multivariate Statistik. Lehr- und Handbuch der angewandten Statistik. 3. Aufl. (R. Oldenbourg; 815 S.) München und Wien (5. Aufl. 1995 )Google Scholar
  27. Hoaglin, D.C., Mosteller, F., and Tukey, J.W. (Eds.; 1985 ): Exploring Data Tables, Trends and Shapes. (Wiley; pp. 527) New YorkzbMATHGoogle Scholar
  28. James, B., James, K.L., and Siegmund, D. (1987): Tests for a changepoint. Biometrika 74, 71–83MathSciNetzbMATHCrossRefGoogle Scholar
  29. Lombard, F. (1987): Rank tests for changepoint problems. Biometrika 74, 615–624MathSciNetzbMATHCrossRefGoogle Scholar
  30. Pettitt, A.N. (1979): A non-parametric approach to the changepoint problem. Applied Statistics 28, 126–135MathSciNetzbMATHCrossRefGoogle Scholar
  31. Schechtman, Edna (1982): A nonparametric test for detecting changes in location. Communications in Statistics-Theory and Methods 11, 1475–1482CrossRefGoogle Scholar
  32. Shaban, S.A. (1980): Change point problem and two-phase regression: an annotated bibliography. International Statistical Review 48, 83–93MathSciNetzbMATHGoogle Scholar
  33. Zacks, S. (1982): Classical and Bayesian approaches to the change-point problem: fixed sample and sequential procedures. Statistique et Analyse des Données 7, 48–81MathSciNetzbMATHGoogle Scholar
  34. Bortz, J. (1985): Lehrbuch der empirischen Forschung. Für Sozialwissenschaftler. 2. Aufl. (Springer; 898 S.) Berlin, Heidelberg, New York, TokyoGoogle Scholar
  35. Bryk, A.S., and Raudenbush, W. (1992): Hierarchical Linear Models. Applications and Data Analysis Methods. (Sage; pp. 265) Newbury/Calif. and LondonGoogle Scholar
  36. Hoaglin, D.C., Mosteller, F., and Tukey, J.W. (Eds.; 1983 ): Understanding Robust and Exploratory Data Analysis. (Wiley; pp. 447) New YorkzbMATHGoogle Scholar
  37. Plewis, I. (1985): Analysing Change: Measurement and Explanation Using Longitudinal Data. (Wiley; pp. 182) Chichester and New YorkGoogle Scholar
  38. Nesselroade, J.R., and Baltes, P.B. (Eds.; 1979 ): Longitudinal Research in the Study of Behavior and Development. (Academic Press; pp. 386) New York, London, Toronto [vgl. auch Psychological Bulletin 88 (1980), 622–637]Google Scholar
  39. Cornell, R.G. (Ed.; 1984 ): Statistical Methods for Cancer Studies. (Statistics, Vol. 51) (M. Dekker; pp. 479) New York and BaselzbMATHGoogle Scholar
  40. Wallenstein, Sylvan, Gould, Madelyn S., and Kleinman, Marjorie (1989): Use of the scan statistic to detect time-space clustering. American Journal of Epidemiology 130, 1057–1064Google Scholar
  41. Selvin, S. (1991): Statistical Analysis of Epidemiologic Data. (Monogr. Epidem. Biostat. 17) (Oxford Univ. Press; pp. 375) New York and Oxford [2nd ed., 1996, pp. 488]Google Scholar
  42. Walter, S.D. (1992): The analysis of regional patterns in health data. I. Distributional considerations. II. The power to detect environmental effects. American Journal of Epidemiology 136, 730–741, 742–759Google Scholar
  43. Wynder, E.L., Szklo, M., Feinleib, M., Stellman, S.D., Stallones, R.A., and Schlesselman, J.J. (1987): Workshop on Guidelines to the Epidemiology of Weak Associations. Preventive Medicine 16, 139–212 [vgl. auch Statistics in Medicine 15 (1996), 681–952]Google Scholar
  44. Cressie, N.A.C. (1991): Statistics for Spatial Data. (Wiley; pp. 900) New York (rev. ed. 1993)Google Scholar
  45. Haining, R. (1993): Spatial Data Analysis in the Social and Environmental Sciences. (Cambridge Univ. Press; pp. 432 ) Cambridge, U.K.Google Scholar
  46. Samet, H. (1990): The Design and Analysis of Spatial Data Structures. (Addison-Wesley; pp. 493 ) Reading, Mass.Google Scholar
  47. Manly, B.F.J., and Parr, M.J.: A new method of estimating population size, survivorship, and birth rate from capture-recapture data. Trans. Soc. Br. Ent. 18 (1968), 81–89 [vgl. Biometrika 56 (1969), 407–410 u. Biometrics 27 (1971), 415–424, 28 (1972), 337–343, 29 (1973), 487–500, 39 (1983), 1035–1049, 40 (1984), 329–340, 42 (1986), 267–292, 45 (1989), 395–413, 46 (1990), 157–162, 52 (1996), 860–873]Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Lothar Sachs
    • 1
  1. 1.KlausdorfDeutschland

Personalised recommendations