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Anna Szczepańska and Ewa Bakinowska

. Technometrics 46(1): 76-86. Horvát I., Kokoszka P. (1997): Change-Point Detection With Nonparametric Regression. Technical Report University of Liverpool. Hárdle W., Müller M., Sperlic, S., Werwatz A. (2004): Nonparametric and Semiparametric Models. Springer. Kayri M., Zirhlioglu G. (2009): Kernel Smoothing Function and Choosing Bandwidth for Non-Parametric Regression Methods. Ozean Journal of Applied Sciences 2(1). Kleemola J. (1998): Modeling crop growth and biomass partitioning to shoots and roots in relation

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Yunlong Gong and Jan de Haan

Method: Applications to Geographical Research.” Geographical Analysis 4: 81–91. DOI: . Clapp, J.M. 2004. “A Semiparametric Method for Estimating Local House Price Indices.” Real Estate Economics 32: 127–160. DOI: . Davis, M.A. and J. Heathcote. 2007. “The Price and Quantity of Residential Land in the United States.” Journal of Monetary Economics 54: 2595–2620. DOI: . Davis, M.A. and M.G. Palumbo

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Bin Liu, Cindy Long Yu, Michael Joseph Price and Yan Jiang

–846. Doi: 10.1093/biomet/92.4.831. Cattaneo, M.D. 2010. “Efficient Semiparametric Estimation of Multi-valued Treatment Effects under Ignorability.” Journal of Econometrics 155(2): 138–154. Doi: 10.1016/j.jeconom.2009.09.023. DuGoff, E., M. Schuler, and E. Stuart. 2014. “Generalizing Observational Study Results: Applying Propensity Score Methods to Complex Surveys.” Health Services Research 49(1): 284–303. Doi: 10.1111/1475-6773.12090. Fuller, W.A. 2009. Sampling Statistics , Vol. 56, John Wiley and Sons. Doi: 10.1002/9780470523551. Hahn, J

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Mohd Faris Dziauddin, Kamarul Ismail and Zainudin Othman

. In: Journal of Urban Economics, Vol. 39, pp. 209-215. Mitchell, D.M., 2000: School quality and housing values. In: Journal of Economics, Vol. 26, pp. 53-68. Nakaya, T., Fotheringham, A.S, Charlton, M.E. and Brunsdon, C., 2009: Semiparametric geographically weighted generalised linear modelling in GWR 4.0, available at:, DoA: 7 March 2014. Ohsfeldt, R.L., 1988: Implicit markets and the demand for housing characteristics. In: Regional Science and Urban Economics

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Andrejs Matvejevs and Jegors Fjodorovs

repartition an dimensionset leurs marges,” Publ. Inst. Statis. Univ. Paris 8,229-231, 1959. [4] W. Darsow, B. Nguyen, E. Olsen, “Copulas and Markov processes,” Illinois Journal of Mathematics 36, 600-642, 1992.. [5] X. Chen, Y. Fan. “Estimation of copula-based semiparametric time series models,” Journal of Econometrics, 2006. [6] D. B. Nelson, “ARCH models as difusion approximations,” Journal of Econometrics, vol. 45, no. 1-2, pp. 7-38, 1990. [7] Y. Ait-Sahalia, R

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Federico Mattia Stefanini and Ottorino-Luca Pantani

: Springer-Verlag. Ruppert D., Wand M., Carroll R. (2003): Semiparametric Regression. Cambridge: Cambridge University Press. Sacchi K.L., Bisson L.F., Adams D.O. (2005): A review of the effect of winemaking techniques on phenolic extraction in red wines. American Journal of Enology and Viticulture 56(3): 197-206. Sarkar D. (2008): Lattice: multivariate data visualization with R. New York: Springer. ISBN 978-0-387-75968-5. <> Soleas G.J., Tomlinson G., Goldberg D.M. (1998

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Satyabrata Pal, Arunava Ghosh and Tapamay Dhar


Economic threshold level (ETL) is an important component in pest management and control. Usually, it is determined by the grower/technologist utilizing his experience on a crop; however, for cereals the values of these indices are available. Knowledge of ETL helps reduce crop loss (and ensure less pesticide application), and as a consequence, profit is increased. Also substantial knowledge is required on the dynamics of the pest population, in order to determine the density at which the economic injury level (EIL) may be prevented (Weersink et al. 1991). This paper is devoted to the development of an analytical method (probabilistic) for determination of ETL, which is defined as the density at which control measures should be determined to prevent an increasing pest population from reaching the economic injury level. A method to model the dynamics of the pest population is also proposed. The above method is demonstrated on a real life data set on pest (whitefly) incidence on betelvine, obtained from an experiment designed for that purpose.

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Tomasz Kossowski and Jan Hauke

. Spatial filtering and eigenvector stability: Space-time models for German unemployment data. Quaderni della facolta di Scienze economiche dell'Universita di Lugano , 0902. Tiefelsdorf M., Griffith D.A., 2007. Semiparametric filtering of spatial autocorrelation: The eigenvector approach. Environment and Planning , A, 39(5): 1193-1221.

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Marko Hannonen

References Anglin, P.M. and Gencay, R. (1996). “Semiparametric Estimation of a Hedonic Price Function”, Journal of Applied Econometrics, Vol. 11, 633-648. Copeland, T.E., Weston, J.F. and Shastri, K. (2005), Financial Theory and Corporate Policy, 4th edition, Addison Wesley. Gencay, R. and Yang, X. (1996). “A Forecast Comparison of Residential Housing Prices by Parametric versus Semiparametric Conditional Mean Estimators”, Economic Letters, Vol. 52, 129-135. George, H. (1879). Progress and

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Noyan Aydin and Taner Akmercan

., Wolfe, D.A. & Chicken, E. (2014). Nonparametric statistical methods (3 rd ed.). New York: John Wiley & Sons, Inc. Hardle, W. (1992). Applied nonparametric regression , Cambridge: Cambridge University Press. Hardle, W., M üller , M., Sperlich, S. & Werwatz, A. (2004). Nonparametric and semiparametric models , Berlin: Springer. Hart, J.D. (1997). Nonparametric smoothing and lack-of-fit tests. New York: Springer. Jacoby, W.G. (2000). Loess: a nonparametric, graphical tool for depicting relationships between variables. Electoral Studies