Advanced SearchSearch Tips
A New Nonparametric Method for Prediction Based on Mean Squared Relative Errors
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
A New Nonparametric Method for Prediction Based on Mean Squared Relative Errors
Jeong, Seok-Oh; Shin, Key-Il;
  PDF(new window)
It is common in practice to use mean squared error(MSE) for prediction. Recently, Park and Shin (2005) and Jones et al. (2007) studied prediction based on mean squared relative error(MSRE). We proposed a new nonparametric way of prediction based on MSRE substituting Jones et al. (2007) and provided a small simulation study which highly supports the proposed method.
Mean squared error;mean squared relative error;prediction;kernel smoothing;
 Cited by
BLS 무응답 보정법을 이용한 대체법과 이월대체법에 관한 연구,이상은;신기일;

응용통계연구, 2010. vol.23. 5, pp.909-921 crossref(new window)
MSPE를 이용한 임금총액 소지역 추정,황희진;신기일;

응용통계연구, 2009. vol.22. 2, pp.403-414 crossref(new window)
Logistic Regression Type Small Area Estimations Based on Relative Error,Hwang, Hee-Jin;Shin, Key-Il;

응용통계연구, 2011. vol.24. 3, pp.445-453 crossref(new window)
준모수혼합모형을 이용한 축소소지역추정,정석오;추만호;신기일;

응용통계연구, 2014. vol.27. 4, pp.605-617 crossref(new window)
Logistic Regression Type Small Area Estimations Based on Relative Error, Korean Journal of Applied Statistics, 2011, 24, 3, 445  crossref(new windwow)
Shrinkage Small Area Estimation Using a Semiparametric Mixed Model, Korean Journal of Applied Statistics, 2014, 27, 4, 605  crossref(new windwow)
Relative Error Prediction via Penalized Regression, Korean Journal of Applied Statistics, 2015, 28, 6, 1103  crossref(new windwow)
조기종, 정석오, 신기일 (2006). 이동-멱변환에 관한 연구. <응용통계연구>, 19, 283-290

Carroll, R. J. (1982). Adapting for heteroscedasticity in linear models. The Annals of Statistics, 10, 1224-1233 crossref(new window)

Fan, J. and Gijbels, I. (1996). Local Polynomial Modelling and Its Applications. Chapman & Hall/CRC, New York

Hall, P. and Carroll, R. J. (1989). Variance function estimation in regression: The effect of estimating the mean. Journal of the Royal Statistical Society, Ser. B, 51, 3-14

Hall. P., Kay, J. W. and Titterington, D. M. (1991). On estimation of noise variance in two-dimensional signal processing. Advances in Applied Probability, 23, 476-495 crossref(new window)

Jeong, S. O. and Kang, K. H. (2006). A difference-based variance function estimator in the multiple regression model, a manuscript

Jones, M. C., Park, H., Shin, K. I., Vines, S. K. and Jeong, S. O. (2007). Relative error prediction via kernel regression smoothers. Journal of Statistical Planning and Inference, accepted

Lee, Y. K., Kim, T. Y. and Park, B. U. (2006). A simple variance estimator in non- parametric regression models with multivariate predictors. Journal of the Korean Statistical Society, 35, 105-114

Park, H. and Shin, K. I. (2006). A shrinked forecast in stationary processes favouring percentage error. Journal of Time Series Analysis, 27, 129-139 crossref(new window)

Park, H. and Stefanski, L. A. (1998). Relative-error prediction. Statistics & Probability Letters, 40, 227-236 crossref(new window)

Rice, J. (1984). Bandwidth choice for nonparametric regression. The Annals of Statistics, 12, 1215-1230 crossref(new window)

Ruppert, D., Wand, M. P., Holst, U. and HÄossjer, O. (1997). Local polynomial variance function estimation. Technometrics, 39, 262-273 crossref(new window)

Spokoiny, V. (2002). Variance estimation for high-dimensional regression models. Journal of Multivariate Analysis, 82, 111-133 crossref(new window)

Wasserman, L. (2006). All of Nonparametric Statistics. Springer, New York

Wei, W. W. S. (1990). Time Series Analysis, Univariate and Multivariate Methods. Addison Wesley, New York