References

Ahmad, I. A. and Lin, P. E. (1976).

Non-parametric sequential estimation of a multiple regression function, Bulletin of Mathematical Statistics 17: 63-75.

Akaike, H. (1970).

Statistical predictor information, Annals of the Institute of Statistical Mathematics 22: 203-17.

Akaike, H. (1974).

A new look at the statistical model identification, IEEE transactions on Automatic Control 19: 716-23.

Akaike, H. (1979).

A bayesian extension of the minimum aic procedure of autoregressive model fitting, Biometrika 66: 237-242.

Ango Nze, P. (1992).

Critères d'ergodicité de quelques modèles à représentation markovienne, C. R. Acad. Sc. Paris 315: 1301-1304.

Auestad, B. and Tjøstheim, D. (1990).

Identification of nonlinear time series: First order characterization and order estimation, Biometrika 77: 669-687.

Azzalini, A. (1984).

Estimation and hypothesis testing of autoregressive time series, Biometrika 71: 85-90.

Baillie, R. and Bollerslev, T. (1989).

Intra-day and inter-market volatility in foreign exchange rates, Review of Economic Studies 45: 745-762.

Bartlett, M. S. (1963).

Statistical estimation of density functions, Sankhy$\bar{a}$, Series A 25: 245-54.

Benedetti, J. K. (1977).

On the nonparametric estimation of regression functions, Journal of the Royal Statistical Society, Series B 39: 248-53.

Bera, A. and Higgins, M. (1993).

A survey of arch models: Properties, estimation and testing, Journal of Econometric Surveys.
in press.

Besse, P. and Ramsay, J. O. (1986).

Principal component analysis of sampled functions, Psychometrika 51: 285-311.

Bierens, H. J. (1983).

Uniform consistency of kernel estimators of a regression function under generalized conditions, Journal of the American Statistical Association 77: 699-707.

Bierens, H. J. (1987).

Kernel estimators of regression functions, Advances in Econometrics, Cambridge University Press.

Billingsley, P. (1968).

Convergence of probability measures, Wiley, New York.

Bollerslev, T. (1986).

Generalized autoregressive conditional heteroscedasticity, Journal of Econometrics 31: 307-327.

Boneva, L. I., Kendall, D. and Stefanov, I. (1970).

Spline transformations: three new diagnostic aids for the statistical data analyst, Journal of the Royal Statistical Society, Series B 32: 1-71.

Bossaerts, P., Härdle, W. and Hafner, C. (1996).

Foreign exchange-rates have surprising volatility, Athens conference on applied probability at time series ted h annan memory volume.
Editor: P. Robinson, M. Rosenblatt, Springer Verlag.

Carroll, R. J. and Härdle, W. (1988).

Symmetrized nearest neighbor regression estimates, Statistics and Probability Letters 7: 315-18.

Cenzov, N. N. (1962).

Evaluation of an unknown distribution density from observations, Soviet Math. Dokl. 3: 1599-62.

Chan, K. S. and Tong, H. (1986).

On estimating thresholds in autoregressive models, Journal of Time Series Analysis 7: 179-190.

Chen, R. and Hafner, C. (1995).

A nonparametric predictor for nonlinear time series, in XploRe - an Interactive Statistical Computing Environment (Härdle et al.; 1995).

Cheng, B. and Tong, H. (1992).

On consistent nonparametric order determination and chaos (with discussion), Journal of the Royal Statistical Society, Series B 54: 427-474.

Cheng, K. F. and Cheng, P. E. (1987).

Robust nonparametric estimation of a regression function, Sankhya : The Indian Journal of Statistics 49: 9-22.

Cheng, K. F. and Lin, P. E. (1981).

Nonparametric estimation of a regression function, Zeitschrift für Wahrscheinlichkeitstheorie und verwandte Gebiete 57: 233-33.

Cheng, P. and Cheng, K. (1990).

Asymptotic normality for robust $r$-estimators of regression function, Journal of Statistical Planning and Inference 24: 137-149.

Clark, R. M. (1980).

Calibration, cross-validation and carbon 14 ii., Journal of the Royal Statistical Society, Series A 143: 177-94.

Cleveland, W. S. and McGill, R. (1984).

The many faces of a scatter plot, Journal of the American Statistical Association 79: 807-22.

Collomb, G. (1977).

Quelques propriétés de la méthode du noyau pour l'estimation non-paramétrique de la régression en un point fixé., C. R. Acad. Sc. Paris 285: 289-92.

Collomb, G. (1981).

Estimation non-paramétrique de la régression: Revue bibliographique, ISI 49: 75-93.

Collomb, G. and Härdle, W. (1986).

Strong uniform convergence rates in robust nonparametric time series analysis and prediction: kernel regression estimation from dependent observations, Stochastic Processes and Applications 23: 77-89.

Collomb, G., Härdle, W. and Hassani, S. (1987).

A note on prediction via estimation of the conditional mode function, Journal of Statistical Planning and Inference 15: 227-36.

Cover, T. M. and Hart, P. E. (1967).

Nearest neighbor pattern classification, IEEE Transactions on Information Theory 13: 21-7.

Dauxois, J., Pousse, A. and Romain, Y. (1982).

Asymptotic theory for the principal component analysis of a vector random function: some applications to statistical inference, Journal of Multivariate Analysis 12: 136-154.

Davydov, Y. A. (1973).

Mixing conditions for markov chains, Theory of Probability and its Applications 18: 312-328.

Deaton, A. (1988).

Agricultural pricing policies and demand patterns in thailand, unpublished manuscript.

Devroye, L. P. (1978a).

The uniform convergence of nearest neighbor regression function estimators and their application in optimization, IEEE Transactions on Information Theory 24: 142-51.

Devroye, L. P. and Györfi, L. (1985).

Distribution-free exponential bound for the $L_1$ error of partitioning estimates of a regression function, Reidel, Dortrecht.

Devroye, L. P. and Wagner, T. J. (1980a).

Distribution free consistency results in nonparametric discrimination and regression function estimation, ANNALS 8: 231-9.

Devroye, L. P. and Wagner, T. J. (1980b).

On the $l_1$-convergence of kernel estimators of regression functions with applications in discrimination, Zeitschrift für Wahrscheinlichkeitstheorie und verwandte Gebiete 51: 15-25.

Diebolt, J. and Guegan, D. (1990).

Probabilistic properties of the general nonlinear autoregressive process of order one, L.S.T.A. 128, Université Paris VI.

Doukhan, P. and Ghindès, M. (1980).

Estimation dans le processus $x_n = f(x_{n-1}) + \varepsilon_n$., C. R. Acad. Sc. Paris 297: 61-4.

Doukhan, P. and Ghindès, M. (1983).

Estimation de la transition de probabilité d'une chaîne de markov doëblin-récurrente. etude cas du processus autorégressif général d'ordre 1, Stochastic Processes and their Applications 15: 271-93.

Engle, R. F. (1982).

Autoregressive conditional heteroscedasticity with estimates of the variance of uk inflation, Econometrica 50: 987-1008.

Engle, R. F., Granger, W. J., Rice, J. and Weiss, A. (1986).

Semiparametric estimates of the relation between weather and electricity sales, Journal of the American Statistical Association 81: 310-20.

Epanechnikov, V. (1969).

Nonparametric estimates of a multivariate probability density, Theory of Probability and its Applications 14: 153-8.

Eubank, R. (1988).

Spline smoothing and nonparametreic regression, Dekker, New York.

Feller, W. (ed.) (1971).

An introduction to probability theory and its applications, Volume II., Wiley, New York.

Friedman, J. (1984).

A variable span smoother, Technical report lcs5, Department of Statistics, Stanford University, Stanford, CA.

Gasser, T. and Müller, H. G. (1979).

Kernel estimation of regression functions, in Gasser and Rosenblatt (eds), Smoothing Techniques for Curve Estimation, Springer Verlag, Heidelberg.

Gasser, T. and Müller, H. G. (1984).

Estimating regression functions and their derivatives by the kernel method, Scandinavian Journal of Statistics 11: 171-85.

Gasser, T., Müller, H. G., Köhler, W., Molinari, L. and Prader, A. (1984a).

Nonparametric regression analysis of growth curves, Annals of Statistics 12: 210-29.

Gasser, T., Müller, H. G., Köhler, W., Molinari, L. and Prader, A. (1984b).

Nonparametric regression analysis of growth curves, Annals of Statistics 12: 210-29.

Gasser, T.and Müller, H. G. and Mammitzsch, V. (1985).

Kernels for nonparametric curve estimation, Journal of the Royal Statistical Society, Series B 47: 238-52.

GAUSS (1987).

GAUSS is a program for PCs available from Aptech Systems, Inc., Product Development, P.O. Box 6487, Kent, WA 98064.

Georgiev, A. A. (1984a).

Nonparametric system identification by kernel methods, IEEE Transactions of Automatic Control 29: 356-8.

Georgiev, A. A. (1984b).

Speed of convergence in nonparametric kernel estimation of a regression function and its derivatives, Annals of the Institute of Statistical Mathematics 36: 455-62.

Good, I. J. and Gaskins, R. A. (1971).

Nonparametric roughness penalties for probability densities, Biometrika 58: 255-77.

Gouriéroux, C. (1992).

Modeles ARCH et Applications Financieres, Economica, Paris.

Gouriéroux, C. and Monfort, A. (1992).

Qualitative threshold arch models, Journal of Econometrics 52: 159-199.

Granger, C. and Teräsvirta, T. (1992).

Modeling Nonlinear Dynamic Relationships, Oxford University Press, Oxford.

Granger, C. W. J. and Anderson, A. P. (1978).

An Introduction to Bilinear Time Series Models, Vandenhoeck & Ruprecht, Göttingen & Zürich.

Greblicki, W. (1974).

asymptotically optimal probabilistic algorithms for pattern recognition and identification, (in polish), Monografie.
Prace Naukowe Instytutu Cybernetyki Technicznej No. 18, Wroclaw.

Greblicki, W. and Krzyzak, A. (1980).

Asymptotic properties of kernel estimates of a regression function, Journal of Statistical Planning and Inference 4: 81-90.

Greblicki, W., Rutkowska, D. and Rutkowski, L. (1983).

An orthogonal series estimate of time-varying regression, Annals of the Institute of Statistical Mathematics 35: 215-28.

Györfi, L. (1981).

The rate of convergence of $k_n$-$nn$ regression estimation and classification, IEEE Transactions of Information Theory 27: 500-9.

Györfi, L., Härdle, W., Sarda, P. and Vieu, P. (1989).

Nonparametric Curve Estimation from Time Series, Vol. 60 of Lecture Notes in Statistics, Springer-Verlag, Heidelberg.

Haggan, V. and Ozaki, T. (1981).

Modeling nonlinear vibrations using an amplitude-dependent autoregressive time series model, Biometrika 68: 189-196.

Härdle, W. (1984).

Robust regression function estimation, Journal of Multivariate Analysis 14: 169-80.

Härdle, W. (1987a).

Resistant smoothing using the fast fourier transform, as 222., Applied Statistics 36: 104-11.

Härdle, W. and Nixdorf, R. (1986).

Nonparametric sequential estimation of zeros and extrema of regression functions, IEEE Transactions of Information Theory IT-33: 367-373.

Härdle, W. and Scott, D. W. (1992).

Smoothing in low and high dimensions by weighted averaging using rounded points.

Härdle, W. and Tsybakov, A. B. (1992).

Robust locally adaptive nonparametric regression, Data Analysis and Statistical Inference - Festschrift in Honour of Friedhelm Eicker, Schach, S. and Trenkler, G. pp. 127-144.

Härdle, W. and Tuan, P. D. (1986).

Some theory on $m$-smoothing of time series, Journal of Time Series Analysis 7: 191-204.

Härdle, W. and Vieu, P. (1992).

Kernel regression smoothing of time series, Journal of Time Series Analysis 13: 209-232.

Härdle, W., Klinke, S. and Turlach, B. (1995).

XploRe - an Interactive Statistical Computing Environment, Springer, Heidelberg.

Hart, D. and Wehrly, T. E. (1986).

Kernel regression estimation using repeated measurements data, Journal of the American Statistical Association 81: 1080-8.

Hart, D. and Wehrly, T. E. (1993).

Consistency of cross-validation when the data are curves, Stochastic Processes and Applications 45: 351-361.

Hart, J. D. (1994a).

Smoothing time-dependent data: a survey of data driven methods, Journal of Nonparametric Statistics.
in press.

Hildenbrand, K. and Hildenbrand, W. (1986).

Contributions to mathematical economics, in K. Hildenbrand and W. Hildenbrand (eds), On the mean income effect: data analysis of the U.K. family expenditure survey, North Holland, New York.

Hildenbrand, W. (1986).

Equilibrium analysis of large economies. talk presented at the international congress of mathematicians, berkeley, california, august 3-11, 1986., Discussion paper a-72, SFB 303, University of Bonn.

Huber, P. J. (1979).

Robust smoothing, in E. Launer and G. Wilkinson (eds), Robustness in Statistics, Academic Press.

ISP (1987).

ISP is a program for PCs available from Artemis Systems Inc.

Jennen-Steinmetz, C. and Gasser, T. (1988).

A unifying approach to nonparametric regression estimation, unpublished manuscript.

Johnston, G. J. (1979).

Smooth nonparametric regression analysis, Institute of Statistics Mimeo Series 1253, University of North Carolina, Chapel Hill, NC.

Johnston, G. J. (1982).

Probabilities of maximal deviations for nonparametric regression function estimates, Journal of Multivariate Analysis 12: 402-14.

Kallieris, D. and Mattern, R. (1984).

Belastbarkeitsgrenze und verletzungsmechanik des angegurteten fahrzeuginsassen beim seitenaufprall. phase i: Kinematik und belastungen beim seitenaufprall im vergleich dummy/leiche, FAT Schriftenreihe 36, Forschungsvereinigung Automobiltechnik e.V. (FAT).

Kendall, M. and Stuart, A. (1979).

The advanced theory of statistics., Vol. 2., Charles Griffin, London.

Lai, S. L. (1977).

Large sample properties of $k$-nearest neighbor procedures, Ph.d. dissertation, Dept. Mathematics, UCLA, Los Angeles.

Leser, C. E. (1963).

Forms of engel functions, Econometrica 31: 694-703.

Liptser, R. S. and Shirjaev, A. N. (1980).

A functional central limit theorem for martingales, Theory of Probability and its Applications 25: 667-688.

Loftsgaarden, D. O. and Quesenberry, G. P. (1965).

A nonparametric estimate of a multivariate density function, Annals of Mathematical Statistics 36: 1049-51.

Lütkepohl, H. (1992).

Multiple Time Series Analysis, Springer-Verlag, Heidelberg.

Mack, Y. P. (1981).

Local properties of $k$-$nn$ regression estimates, SIAM, J. Alg. Disc. Meth. 2: 311-23.

Mahieu, R. and Schotman, P. (1994).

Stochastic volatility and the distribution of exchange rate news, Discussion paper, LIFE, University of Limburg.

Manski, C. F. (1989).

Nonparametric estimation of expectations in the analysis of discrete choice under uncertainty, unpublished manuscript.

Masry, E. and Tjøstheim, D. (1992).

Non-parametric estimation and identification of arch and arx nonlinear time series: convergence properties and rates, Preprint, Dept. of Mathematics, University of Bergen.

Mattern, R., Bösche, J., Birk, J. and Härdle, W. (1983).

Fortschritte der Rechtsmedizin, in Froberg, Barz, Bösche, Käppner and Mattern (eds), Experimentelle Untersuchungen zum Verlauf der Alkoholkurve in der späteren Eliminationsphase, Springer-Verlag, Heidelberg.

Müller, H. G. (1987).

Weighted local regression and kernel methods for nonparametric curve fitting, Journal of the American Statistical Association 82: 231-8.

Nadaraya, E. A. (1964).

On estimating regression, Theory Prob. Appl. 10: 186-90.

Nicholls, D. F. and Quinn, B. G. (1982).

Random Coefficient Autoregressive Models: an Introduction, Vol. 11 of Lecture Notes in Statistics, Springer-Verlag, New York.

Nummelin, E. and Tuominen, P. (1982).

Geometric ergodicity of harris-recurrent markov chains with application to renewal theory, Stochastic Processes and Applications 12: 187-202.

Nussbaum, M. (1985).

Spline smoothing in regression models and asymptotic efficiency in $l_2$, Annals of Statistics 13: 984-97.

Parzen, E. (1962).

On estimation of a probability density and mode, Annals of Mathematical Statistics 35: 1065-76.

Pezzuli, S. and Silverman, B. W. (1993).

Some properties of smoothed principal component analysis for functional data, Computational Statistics 8: 1-16.

Pflug, G. (1985).

Neuere Verfahren der nichtparametrischen Statistik, in M. Jørgensen, C. T. Nielsen, N. Keiding and N. E. Skakkebaek (eds), Parametrische und Nichtparametrische Modelle für Wachstumsdaten, Springer-Verlag, Heidelberg.
(English version available as Research Report 85/3 from the Statistical Research Unit, University of Copenhagen).

Pham, D. T. (1985).

Bilinear markovian representations and bilinear models, Stochastic Processes and Applications 20: 295-306.

Pourciau, B. H. (1980).

Modern multiplier rules, American Mathematical Monthly 6: 433-52.

Priestley, M. B. (1988).

Nonlinear and Nonstationary Time Series Analysis, Academic Press, New York.

Priestley, M. B. and Chao, M. T. (1972).

Nonparametric function fitting, Journal of the Royal Statistical Society, Series B 34: 385-92.

Ramsay, J. O. (1982).

When the data are functions, Psychometrika 47: 379-396.

Raz, J., Turetsky, B. and Fein, G. (1989).

Selecting the smoothing parameter for estimation of slowly changing evoked potential signals, Biometrics 45: 745-762.

Reinsch, H. (1967).

Smoothing by spline functions, Numerische Mathematik 10: 177-83.

Revesz, P. (1976).

Robbins-monro procedure in a hilbert space and its applications in the theorie of learning process i, Studia Sci. Math. Hung. pp. 391-8.

Revesz, P. (1977).

How to apply the method of stochastic approximation in the nonparametric estimation of a regression function, Mathematische Operationsforschung, Serie Statistics 8: 119-26.

Rice, J. A. and Silverman, B. W. (1991).

Estimating the mean and covariance structure nonparametrically when the data are curves, Journal of the Royal Statistical Society, Series B 53: 233-243.

Robinson, P. M. (1983).

Nonparametric estimators for time series, Time Series Analysis 4: 185-207.

Robinson, P. M. (1984b).

Kernel estimation and interpolation for time series containing missing observations, Annals of the Institute of Statistical Mathematics 36: 403-417.

Robinson, P. M. (1986).

On the consistency and finite-sample properties of nonparametric kernel time series regression, autoregression and density estimators, Annals of the Institute of Statistical Mathematics 38: 539-549.

Rosenblatt, M. (1956).

Remarks on some nonparametric estimates of a density functions, Annals of Mathematical Statistics 27: 642-69.

Rutkowski, L. (1981).

Sequential estimates of a regression function by orthogonal series with applications in discrimination, in Revesz, Schmetterer and Zolotarev (eds), The First Pannonian Symposium on Mathematical Statistics, Springer-Verlag, pp. 263-44.

Rutkowski, L. (1985b).

Real-time identification of time-varying systems by nonparametric algorithms based on parzen kernels, International Journal of Systems Science 16: 1123-30.

Rutkowski, L. (ed.) (1985a).

Nonparametric identification of quasi-stationary systems, North Holland, New York.

S (1988).

See Becker, Chambers and Wilks, (1988).

Schmerling, S. and Peil, J. (1985).

Verfahren der lokalen approximation zur nichtparametrischen schätzung unbekannter stetiger funktionen aus meßdaten., Gegenbaurs morphologisches Jahrbuch Leipzig 131: 367-81.

Schmerling, S. and Peil, J. (1986).

Improvement of the method of kernel estimation by local polynomial approximation of the empirical distribution function and its application to empirical regression, Gegenbaurs morphologisches Jahrbuch Leipzig 132: 29-35.

Schoenberg, I. J. (1964).

Spline functions and the problem of graduation, Mathematics 52: 974-50.

Schönfeld, P. (ed.) (1969).

Methoden der Ökonometrie, Verlag Franz Vahlen GmbH, Berlin und Frankfurt a.M.

Serfling, R. (ed.) (1980).

Approximation theorems of mathematical statistics, Wiley, New York.

Silverman, B. W. (1984).

Spline smoothing: the equivalant variable kernel method, Annals of Statistics 12: 898-916.

Silverman, B. W. (1985).

Some aspects of the spline smoothing approach to nonparametric regression curve fitting (with discussion), Journal of the Royal Statistical Society, Series B 47: 1-52.

Singh, R. and Ullah, A. (1985).

Nonparametric time series estimation of joint dgp, conditional dgp and vector autoregression, Econometric Theory.

Stone, C. J. (1977).

Consistent nonparametric regression (with discussion), Annals of Statistics 5: 595-645.

Stute, W. (1984).

Asymptotic normality of nearest neighbor regression function estimates, Annals of Statistics 12: 917-26.

Subba Rao, T. (1981).

On the theory of bilinear time series models, Journal of the Royal Statistical Society, Series B 43: 244-255.

Subba Rao, T. and Gabr, M. M. (1980).

An Introduction to Bispectral Analysis and Bilinear Time Series Models, Vol. 24 of Lecture Notes in Statistics, Springer-Verlag, New York.

Survey, F. E. (1968-1983).

Annual Base Tapes, Department of Employment, Statistics Division, Her Majesty's Stationery Office, London.
The data utilized in this book were made available by the ESRC Data Archive at the University of Essex.

Szegö, G. (1959).

Orthogonal polynomials, Amer. Math. Soc. Coll. Publ.

Tapia, D. and Thompson, J. (eds) (1978).

Nonparametric probability density estimation, The Johns Hopkins University Press, Baltimore, MD.

Tjøstheim, D. (1990).

Nonlinear time series and markov chains, Advanced Applied Probability 22: 587-611.

Tjøstheim, D. (1994).

Nonlinear time series, a selective review, Scandinavian Journal of Statistics 21: 97-130.

Tjøstheim, D. and Auestad, B. (1994b).

Nonparametric identification of nonlinear time series: Selecting significant lags, Journal of the American Statistical Association 89(428): 1410-1419.

Tong, H. (1978).

On a threshold model, in C. H. Chen (ed.), Pattern Recognition and Signal Processing, Sijthoff and Noordolf, The Netherlands.

Tong, H. (1983).

Threshold Models in Nonlinear Time Series Analysis, Vol. 21 of Lecture Notes in Statistics, Springer-Verlag, Heidelberg.

Tong, H. (1990).

Nonlinear Time Series Analysis: A Dynamic Approach, Oxford University Press, Oxford.

Truong, Y. K. (1993).

A Nonparametric Framework for Time Series Analysis, Springer, New York.

Truong, Y. K. and Stone, C. J. (1987b).

Nonparametric time series prediction: kernel estimators based on local medians, unpublished manuscript.

Tsybakov, A. B. (1986).

Robust reconstruction of functions by the local approximation method, (in russian), 12: 69-84.
English translation in: Problems of information transmission, 22, 133-46, New York: Plenum.

Tsybakov, A. B. (1988).

Passive stochastic approximation, Discussion paper, University Bonn, SFB 303.

Tufte, G. (1983).

The visual display of quantitive information, Graphics, New Haven.

Tukey, J. W. (1947).

Nonparametric estimation ii. statistically equivalent blocks and tolerance regions. the continuous case, Annals of Mathematical Statistics 18: 529-39.

Tukey, J. W. (1961).

Curves as parameters and toch estimation, Proc 4th Berkeley Symposium pp. 681-94.

Tukey, J. W. (ed.) (1977).

Exploratory data analysis, Addison Welsley, Reading, MA.

Tweedie, R. L. (1975).

Sufficient conditions for ergodicity and recurrence of markov chain on a general state space, Stochastic Processes and Applications 3: 385-403.

Ullah, A. (1987).

Nonparametric estimation of econometric funktionals, unpublished manuscript.

Wahba, G. (1975).

Optimal convergence properties of variable knot, kernel, and orthogonal series methods for density estimation, Annals of Statistics 3: 15-29.

Wahba, G. (1977).

Applications of statistics, in P. Krishnaiah (ed.), A survey of some smoothing problems and the method of generalized cross-validation for solving them, North Holland, Amsterdam.

Wahba, G. (1979).

Convergence rates of ``thin plate" smoothing splines when the data are noisy, in T. Gasser and M. Rosenblatt (eds), Smoothing techniques for curve estimation, Springer-Verlag, New York.

Wahba, G. (1980).

Automatic smoothing of the log periodogram, Journal of the American Statistical Association 75: 122-32.

Walter, G. (1977).

Properties of hermite series estimation of probability density, Annals of Statistics 5: 1258-64.

Watson, G. S. (1964).

Smooth regression analysis, Sankhy$\bar{a}$, Series A 26: 359-72.

Whittaker, E. T. (1923).

On a new method graduation, Proc. Edinburgh Math. Soc. 41: 63-75.

XploRe (1989).

See Härdle (1987b) and Broich, Härdle and Krause (1989).

Yakowitz, S. (1985a).

Nonparametric density estimation, prediction, and regression for markov sequences, Annals of Statistics 80: 215-21.

Yakowitz, S. (1985b).

Markov flow models and the flood warning problem, Water Resources Research 21: 81-8.

Yakowitz, S. (1987).

Nearest neighbor methods for time series analysis, Journal of Time Series Analysis 18: 1-13.

Yakowitz, S. and Szidarovsky, F. (eds) (1986).

An introduction to numerical computations, Macmillan, New York.

Yang, S. (1981).

Linear functions of concomitants of order statistics with application to nonparametric estimation of a regression function, Journal of the American Statistical Association 76: 658-62.