应用统计文献库(一)
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摘要:包括(1)经典学术论文(2)经典文献(3)经典论文下载
 

应用统计经典学术论文

1.         Baicheng Chen, YaoYu et al. Profiled adaptive Elastic-Net procedure for partially linear models with high-dimensional covariates Journal of Statistical Planning and Inference, 142(2012)1733–1745

2.         Rongning Wu, QinWang, Shrinkage estimation for linear regression with ARMA errors, Journal of Statistical Planning and Inference, 142(2012)2136–2148

3.         Trevor Hastie, Forward stagewise regression and the monotone lasso, Electronic Journal of Statistics,Vol. 1 (2007) 1–29

4.         Jian Huang, Patrick Breheny and Shuangge Ma, A Selective Review of Group Selection in High-Dimensional Models, Statistical Science,2012, Vol. 27, No. 4, 481–499

5.         Yaya Xie, Xiu Li, et al. Customer churn prediction using improved balanced random forests,  Expert Systems with Applications 36 (2009) 54455449

6.         J. Burez, D. Van den Poel, Handling class imbalance in customer churn prediction, Expert Systems with Applications 36 (2009) 4626–4636

7.         BRADLEY EFRON, TREVOR HASTIE, et al. LEAST ANGLE REGRESSION, The Annals of Statistics, 2004, Vol. 32, No. 2, 407–499)

8.         MIROSLAV KUBAT, ROBERT C. HOLTE ,STAN MATWIN, Machine Learning for the Detection of Oil Spills in Satellite Radar Images, Machine Learning, 30, 195–215 , 1998

9.         Yanmin Sun, Mohamed S. Kamel, Andrew K.C.Wong,YangWang,  Cost-sensitive boosting for classification of imbalanced data, Pattern Recognition 40 (2007) 3358 – 3378

10.     Heckman N. Spline smoothing in partly linear models. J. Roy. Statist.Soc. B. 1986, 48: 244 -248.

11.     Speckman P. Kernel smoothing in partial linear models. J. Amer. Statist.Soc. Ser. B. 1988, 50: 413-436

12.     Robinson P. Root-N-consistent semiparametric regression models. Econometrica. 1988, 56: 931-954

13.     Yohai V J, Maronna R A. Asymptotic behavior of M-estimators for the linear model. The Annals of Statistics. 1979: 258-268.

14.     Fan J Q, Li R, Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association, 2001,96 :1348-1360.

15.     Mammen E, Geer S V D. Penalized quasi-likelihood estimation in partial linear models. The Annals of Statistics. 1991,25:1014-1035

16.     Hastie T, Tibshirani R. Varying-coefficient models. J. Roy. Stat. Soc.Ser. B. 1993, 55: 757-796

17.     Fan J Q, Zhang W Y. Statistical estimation in varying coefficient models. The Annals of Statistics.1999, 27: 1491-1518

18.     Buckley J, James J. Linear Regression with censored data. Biometrika.1979, 66:429-436

19.     James G M, Wang J, Zhu J. Functional linear regression that's interpretable. The Annals of Statistics. 2009: 2083-2108.

20.     Hjort N, Claeskens G. Frequentist model average estimators. Journal of the American Statistical Association, 2003, 98: 879-945.

21.     Koul H, Susarla V, Van R J. Regression analysis with randomly right-censored data. Ann. Statist. 1981, 9: 1276-1288

22.     Ller M U , Stadtm U, Ller U. Generalized functional linear models. Annals of Statistics. 2005: 774-805.

23.     Wang Q H, Linton O, HÄardle W. Semiparametric regression analysis with missing response at random. Journal of the American Statistical Association. 2004, 99: 334-345

24.     Ramsay J O, Li X. Curve registration. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2002, 60(2): 351-363.

25.     Croux C, Haesbroeck G. Principal component analysis based on robust estimators of the covariance or correlation matrix: influence functions and efficiencies. Biometrika. 2000, 87(3): 603-618.

26.     Wang L, Wu Y, Li R. Quantile regression for analyzing heterogeneity in ultra-high dimension. Journal of the American Statistical Association. 2012, 107(497): 214-222.

27.     Kai B, Li R, Zou H. New efficient estimation and variable selection methods for semiparametric varying-coefficient partially linear models. Annals of statistics. 2011, 39(1): 305.

28.     Kai B, Li R, Zou H. Local composite quantile regression smoothing: an efficient and safe alternative to local polynomial regression. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2010, 72(1): 49-69.

29.     Zou H, Yuan M. Composite quantile regression and the oracle model selection theory. The Annals of Statistics. 2008, 36(3): 1108-1126.

30.     Huang J. Efficient estimation for the proportional hazards model with interval censoring[J]. The Annals of Statistics. 1996, 24(2): 540-568.

31.     Bach F R. Consistency of the group lasso and multiple kernel learning. The Journal of Machine Learning Research. 2008, 9: 1179-1225.

32.     Knight K, Fu W. Asymptotics for lasso-type estimators. Annals of Statistics. 2000: 1356-1378.

33.     Liang H, Wu H. Parameter estimation for differential equation models using a framework of measurement error in regression models. Journal of the American Statistical Association. 2008, 103(484).

34.     Horv A, Th L, Kokoszka P, Reeder R. Estimation of the mean of functional time series and a two-sample problem. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2013, 75(1): 103-122.

35.     SzE Kely G J, Rizzo M L, Bakirov N K. Measuring and testing dependence by correlation of distances. The Annals of Statistics. 2007, 35(6): 2769-2794.

36.     Liang H, HÄardle W, Carroll R J. Estimation in a semiparametric partially linear errors-in- variables model. The Annals of Statistics. 1999, 27:1519-1535

37.     Hansen B. Least squares model averaging. Econometrica, 2007,4: 1175-1189.

38.     Longford N T. Editorial: Model selection and efficiency—is ‘Which model ?’ the right question?. Journal of the Royal Statistical Society A, 2005,(3) :469-472.

39.     Yao F, M U Ller H G, Wang J L. Functional linear regression analysis for longitudinal data. The Annals of Statistics. 2005, 33(6): 2873-2903.

40.     Li K. Sliced inverse regression for dimension reduction. Journal of the American Statistical Association. 1991, 86(414): 316-327.

41.     Ma Y, Zhu L. Efficient estimation in sufficient dimension reduction. The Annals of Statistics. 2013, 41(1): 250-268.

42.     Ma Y, Zhu L. Doubly robust and efficient estimators for heteroscedastic partially linear single-index models allowing high dimensional covariates. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2013, 75(2): 305-322.

43.     He X, Wang L, Hong H G. Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data. The Annals of Statistics. 2013, 41(1): 342-369.

44.     Claeskens G., Hjort N. The focused information criterion (with discussion). Journal of the American Statistical Associa- tion,  2003, 98, 900-916.

45.     Jiang Q, Wang H, Xia Y, et al. On a principal varying coefficient model. Journal of the American Statistical Association. 2013, 108(501): 228-236.

46.     Xia Y, Tong H, Li W K, et al. An adaptive estimation of dimension reduction space. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2002, 64(3): 363-410.

47.     Hoeting J A, Madigan D, Raftery A E,  Volinsky C T. Bayesian model averaging: A tutorial. Statistical Science, 1999,14: 382-417.

48.     Tibshirani R. Regression shrinkage and selection via the lasso. J. Roy. Statist. Soc.Ser. B, 1996, 58 :267-288.

49.     Chen H. Convergence rates for parametric components in a partly linear model. Annals of Statistics, 1988, 16:136-146.

50.     Mammen E, Marron J S, Turlach B A,Wand M P. A general projection framework for constrained smoothing. Statist. Sci., 2001, 16: 232–248.

51.     Jin Z, Ying Z, Wei L J. A simple resampling method by perturbing the minimand. Biometrika, 2001,88: 381- 390.

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