A FAST PROCEDURE OF VARIABLE SELECTION IN LINEAR REGRESSION MODEL

In many situations,we are interested in selection of important variables whichare adequate for prediction under a linear regression model.In this paper,a fast selectionprocedure is proposed and is proved to be strongly consistent.Also,the convergence rate ofmisjudgement probability is given.     

ON ASYMPTOTIC NORMALITY OF PARAMETERS IN MULTIPLE LINEAR ERRORS－IN－VARIABLES MODEL

This paper studies the parameter estimation of multiple dimensional linear errors-in-variables (EV) models in the case where replicated observations are available in some experimental points. Asymptotic normality is established under mild conditions, and the parameters entering the asymptotic variance are consistently estimated to render the result useable in the construction of large-sample confidence regions.     

OPTIMAL GLOBAL RATES OF CONVERGENCE OF M－ESTIMATES FOR NONPARAMETRIC REGRESSION

ＯＰＴＩＭＡＬＧＬＯＢＡＬＲＡＴＥＳＯＦＣＯＮＶＥＲＧＥＮＣＥＯＦＭ－ＥＳＴＩＭＡＴＥＳＦＯＲＮＯＮＰＡＲＡＭＥＴＲＩＣＲＥＧＲＥＳＳＩＯＮＳＨＩＰｅｉｄｅ；ＬＩＧｕｏｙｉｎｇ（ＩｎｓｔｉｔｕｔｅｏｆＳｙｓｔｅｍｓＳｃｉｅｎｃｅ，ＡｃａｄｅｍｉａＳｉ...     

ADMISSIBILITY OF LINEAR ESTIMATORS OF REGRESSION COEFFICIENTS IN THE CLASS OF ALL ESTIMATORS UNDER THE MATRIX LOSS FUNCTION

For the general fixed effects linear model:Y=Xτ+ε,ε～N(0,V)(or N(0,σ~2V)),V≥0,we obtain the necessary and sufficient condition for LY+α to be admissible for Sτ in the class of allestimators under matrix loss function(d-Sτ)(d-Sτ)′;for the general random effects linear model:Y=Xβ+ε,(?)where (?)=XV_(11)X′+XV_(12)+V_(21)X′+V_(22)≥0,we also obtain the necessary and sufficient condition for LY+α to be admissible for Sα+Qβin the class of all estimators under matrix loss function(d-Sα-Qβ)(d-Sα-Qβ)′.     

THE RELATION BETWEEN nTH MINIMAL ERRORS OF TWO CLASSES OF INFORMATION FOR APPROXIMATION IN HOLDER SPACE

Let be Holder space and G = L2([0, 1]d) with the inner product given byThis paper considers the embedding operator S : H →G,S(f) = f, f ∈ H . We prove thatwhere en(S,Astd ) and en(S, Aall ) denote the nth minimal error of standard and linear information respectively in the worst case, average case and randomized settings, and C is a constant.     

ASYMPTOTIC NORMALITY OF SOME ESTIMATORS IN A FIXED-DESIGN SEMIPARAMETRIC REGRESSION MODEL WITH LINEAR TIME SERIES ERRORS

Consider a semiparametric regression model with linear time series errors Yk = x'kβ g(tk) εk, 1 < k < n, where Yk's are responses, xk = (xk1,xk2,..... ,xkp)' and tk ∈T ∪R are fixed design points, β= (β1, β2, ....., βp)' is an unknown parameter vector, g(.) is an unknown bounded real-valued function defined on a compact subset T of the real line R, and εk is a linear process given by εk = ∑j=0∞j(?)ek-j,(?)= 1, where , are i.i.d. random variables. In this paper we establish the asymptotic normality of the least squares estimator of β, a smooth estimator of g(.), and estimators of the autocovariance and autocorrelation functions of the linear process εk.     

STABILITY OF STEADY－STATE SOLUTIONS OF THE COMPETITION MODEL IN THE CHEMOSTAT

ＳＴＡＢＩＬＩＴＹＯＦＳＴＥＡＤＹ－ＳＴＡＴＥＳＯＬＵＴＩＯＮＳＯＦＴＨＥＣＯＭＰＥＴＩＴＩＯＮＭＯＤＥＬＩＮＴＨＥＣＨＥＭＯＳＴＡＴ￥ＷＵＪｉａｎｈｕａ（Ｘｉ＇ａｎＩｎｓｔｉｔｕｔｅｏｆＨｉｇｈｗａｙ，Ｘｉ，ａｎ７１００６４，Ｃｈｉｎａ）ＬＩＹａｎ...     

ON CONVERGENCE AND RATE OF CONVERGENCE OF ALGORITHMS IN THE MOVER－STAYER MODEL

ＯＮＣＯＮＶＥＲＧＥＮＣＥＡＮＤＲＡＴＥＯＦＣＯＮＶＥＲＧＥＮＣＥＯＦＡＬＧＯＲＩＴＨＭＳＩＮＴＨＥＭＯＶＥＲ－ＳＴＡＹＥＲＭＯＤＥＬＷＡＮＧＤｏｎｇｑｉａｎ；Ｌａｗｏｋｏ，Ｃ．Ｒ．Ｏ．（ＤｅｐａｒｔｍｅｎｔｏｆＳｔａｔｉｓｔｉｃｓ，ＭａｓｓｅｙＵｎ...     

GLOBAL AND LOCAL BIFURCATION IN PERTURBATIONS OF NON－SYMMETRY AND SYMMETRY OF HAMILTONIAN SYSTEM

ＧＬＯＢＡＬＡＮＤＬＯＣＡＬＢＩＦＵＲＣＡＴＩＯＮＩＮＰＥＲＴＵＲＢＡＴＩＯＮＳＯＦＮＯＮ－ＳＹＭＭＥＴＲＹＡＮＤＳＹＭＭＥＴＲＹＯＦＨＡＭＩＬＴＯＮＩＡＮＳＹＳＴＥＭ￥ＬＩＵＺｈｅｎｇｒｏｎｇ（ＩｎｓｔｉｔｕｔｅｏｆＡｐｐｌｉｅｄＭａｔｈｅｍａｔｉ...     

ESTIMATION OF A KIND OF JUMP REGRESSION FUNCTION

Research on jump regression func(?)ons has not been adequate yet According to theinformation about the number of jumps,their positions and jump magnitudes,jump regressionfunctions can be classified into eight types.This paper deals especially with the secondjump regression function.First of all,a concept of trimmed spline estimate is proposed andwith it an L~2-consistent estimate of the smoothing part of the jump regression function isobtained.This along with the L~2-consistent estimate of jump magnitude constitutes an estimateof the second jump regression function.This paper discusses also the case that the jumppositions have some indeterminacy.A new criterion is suggested and its unique solutionderived.In the end,a few numerical results are given.     

ON B-SPLINE M-ESTIMATORS IN A SEMIPARAMETRIC REGRESSION MODEL

ＯＮＢ－ＳＰＬＩＮＥＭ－ＥＳＴＩＭＡＴＯＲＳＩＮＡＳＥＭＩＰＡＲＡＭＥＴＲＩＣＲＥＧＲＥＳＳＩＯＮＭＯＤＥＬ￥ＳＨＩＰｅｉｄｅ（ＤｅｐａｒｔｍｅｎｔｏｆＰｒｏｂａｂｉｌｉｔｙａｎｄＳｔａｔｉｓｔｉｃｓ，ＰｅｋｉｎｇＵｎｉｖｅｒｓｉｔｙ，Ｂｅｉｊｎｇ１...     

SEMIPARAMETRIC MODEL SELECTION IN LARGE SAMPLES

1 IntroductionThere are obvious reasons for the popularity of linear regression among which are illter-pretation and simplicity in computation. But this does not mean that a llnear relationship isalways sufficient. In some aPpllcations, the mean is linearly related to some variables but therelation to additiona1 variables are not easily parameterized. Partly linear models become thenatural choices in such aPplicationst the linear model is drinimally altered to allow one or a fewof the indepen…     

VARIABLE SELECTION FOR COVARIATE ADJUSTED REGRESSION MODEL

This paper employs the SCAD-penalized least squares method to simultaneously select variables and estimate the coefficients for high-dimensional covariate adjusted linear regression models. The distorted variables are assumed to be contaminated with a multiplicative factor that is determined by the value of an unknown function of an observable covariate. The authors show that under some appropriate conditions, the SCAD-penalized least squares estimator has the so called ＂oracle property＂. In addition, the authors also suggest a BIC criterion to select the tuning parameter, and show that BIC criterion is able to identify the true model consistently for the covariate adjusted linear regression models. Simulation studies and a real data are used to illustrate the efficiency of the proposed estimation algorithm.     

ROBUST NONPARAMETRIC REGRESSION BASED ON L_1-NORM AND B-SPLINES

ＲＯＢＵＳＴＮＯＮＰＡＲＡＭＥＴＲＩＣＲＥＧＲＥＳＳＩＯＮＢＡＳＥＤＯＮＬ＿１－ＮＯＲＭＡＮＤＢ－ＳＰＬＩＮＥＳＳＨＩＰｅｉｄｅ（ＤｅｐａｒｔｍｅｎｔｏｆＰｒｏｂａｂｉｌｉｔｙａｎｄＳｔａｔｉｓｔｉｃｓ，ＰｅｋｉｎｇＵｎｉｖｅｒｓｉｔｙ，Ｂｅｉｊｉｎ...     

ASSESSMENT OF LOCAL INFLUENCE FOR F-TEST IN LINEAR REGRESSION MODEL

1.IntroductionTheaimofthediagnosticsandinfluenceanalysisistoidentifytheoutliersorinfluelltialobservationsthatsubstantiallyaffecttheparameterestimatesandstatisticalinference.Therelatedworkthathasreceivedconsiderableattentioninlinearregressionmodelistheinfluenceontheregressioncoefficients(see[l--4]).Theinfluencefortheotheraspectsoflinearregressionmodelsuchasconfidenceregionshasalsobeenconsideredby[5].However,theinfluenceonFiestreceivedlessattentionasweknowtheF-testisverysensitivetotheinfluentia…     

MUCLTIVARIATE RESISTANT REGRESSION SPLINES FOR ESTIMATING MULTIVARIATE FUNCTIONS FROM NOISY DATA

1.IntroductionMultivariatesurfaceestimationisanimportantproblemforwhichseveralmethodologieshavebeendevelopedwiththeaimofconstructinganestimatorg,,(x)ofaJ--dimensionalsurfacego(x)basedonnoisyobservations{(Xi,K),15i5n}.Existingestimatorsofgoareobtainedonthe…     

STRONG CONSISTENCY OF M-ESTIMATES OF MULTIPLE REGRESSION COEFFICIENTS

ＳＴＲＯＮＧＣＯＮＳＩＳＴＥＮＣＹＯＦＭ－ＥＳＴＩＭＡＴＥＳＯＦＭＵＬＴＩＰＬＥＲＥＧＲＥＳＳＩＯＮＣＯＥＦＦＩＣＩＥＮＴＳＣＨＥＮＸｉｒｕ（ＧｒａｄｕａｔｅＳｃｈｏｏｌｏｆｔｈｅＣｈｉｎｅｓｅＡｃａｄｅｍｙｏｆＳｃｉｅｎｃｅｓ，Ｂｅｉｊｉｎｇ１００...