数理统计文献库(一)
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摘要:包括1.经典学术论文;2.经典著作 3.经典学术论文(下载) .
 

数理统计经典学术论文

1.         Akaike H. Maximum likelihood identification of gaussian autoregressive moving average models. Biometrika, 1973,22: 203-217.

2.         Fan J Q. Local linear regression smoothers and their mini-max efficiencies. Ann. Statist. 1993, 21(1): 196-216

3.         Schwartz G. Estimating the dimension of a model. The Annals of Statistics,1978,6: 461-464.

4.         Mallows C L. Some comments on Cp. Technometrics, 1973,15:661-675.

5.         Owen A. 1990. Empirical likelihood ratio confidence regions. The Annals of Statistics. 18:90-120.

6.         Owen A. 1991. Empirical likelihood for linear models. The Annals of Statistics. 19: 1725- 1747.

7.         Engle R F, Granger C W J, Rice J, Weiss A. Semiparametric estimates of the relation between weather and electricity scales. J. Amer. Statist.Assoc. 1986, 81: 310-320

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

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

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

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

12.     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.

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

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

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

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

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

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

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

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

21.     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

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

23.     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.

24.     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.

25.     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.

26.     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.

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

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

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

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

31.     Leurgans S. Linear modes, random censoring and aynthetic data. Biometrika. 1987, 74:301-309

32.     Gill R D. Large Sample behavior of the product-limit estimator on the Whole Line. Ann. Statist. 1983, 11:49-58

33.     Zamar R H. Robust estimation in the errors-in-variables model. Biometrika. 1989,76(1): 149-160.

34.     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).

35.     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.

36.     Yao W, Li R. New local estimation procedure for a non-parametric regression function for longitudinal data. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2013, 75(1): 123-138.

37.     Xing H, Ying Z. A Semiparametric change-point regression model for longitudinal observations. Journal of the American Statistical Association. 2012, 107(500): 1625-1637.

38.     Shao X, Zhang X. Testing for change points in time series. Journal of the American Statistical Association. 2010, 105(491).

39.     Bali J L, Boente G, Tyler D E, et al. Robust functional principal components: A projection-pursuit approach. The Annals of Statistics. 2012, 39(6): 2852-2882.

40.     Zamar R H. Robust Estimation in the Errors-in-variables Model. Biometrika. 1989,76(1): 149-160.

41.     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.

42.     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

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

44.     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.

45.     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.

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

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

48.     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.

49.     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.

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

51.     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.

52.     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.

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

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

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

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

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

58.     Wright F T. The asymptotic behavior of monotone regression estimates. The Annals of Statistics, 1981, 9: 443- 448.

59.     Brunk H D. Maximum likelihood estimates of monotone parameters. Ann. Math. Statist., 1955, 26:607-616.

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