Local linear multiple regression with variable. bandwidth in the presence of heteroscedasticity

Transcription

1 Local linear multiple regression with variable bandwidth in the presence of heteroscedasticity Azhong Ye 1 Rob J Hyndman 2 Zinai Li 3 23 January 2006 Abstract: We present local linear estimator with variable bandwidth for multivariate nonparametric regression. We prove its consistency and asymptotic normality in interior and obtain its rates of convergence. For heteroscedastic models, the local linear estimator with variable bandwidth has better goodness of fit than the local linear estimator with constant bandwidth. JEL Classification: C12; C15; C52 Keywords: heteroscedasticity; kernel smoothing; local linear regression; variable bandwidth. 1 Azhong Ye, College of Management, Fuzhou University, Fuzhou, , China. ye2004@fzu.edu.cn 2 Rob J Hyndman, Department of Econometrics and Business Statistics, Monash University, Clayton, Victoria 3800, Australia. Rob.Hyndman@buseco.monash.edu.au 3 Zinai Li, School of Economics and Management, Tsinghua University, Beijin, , China. lizinai@mail.tsinghua.edu.cn 1

2 1 Introduction We are interested in the problem of heteroscedasticity in nonparametric regression, especially when applied to economic data. Heteroscedasticity is very common in economics, and heteroscedasticity in linear regression is covered in almost every econometrics tetbook. Applications of nonparametric regression in economics are growing, and Yatchew (1998) argued that it will become an indispensable tool for every economist because it typically assumes little about the shape of the regression function. Consequently, we believe that heteroscedasticity in nonparametric regression is an important problem that has received limited attention to date. We seek to develop new estimators that have better goodness of fit than the common estimators in nonparametric econometric models. In particular, we are interested in using heteroscedasticity to improve nonparametric regression estimation. There have been a few papers on related topics. Testing for heteroscedasticity in nonparametric regression has been discussed by Eubank and Thomas (1993) and Dette and Munk (1998). Ruppert and Wand (1994) discussed multivariate locally weighted least squares regression when the variances of the disturbances aren t constant. Ruppert, Wand, Ulla and Hossjer (1997) presented the local polynomial estimator of the conditional variance function in a heteroscedastic, nonparametric regression model using linear smoothing of squared residuals. Sharp-optimal and adaptive estimators for heteroscedastic nonparametric regression using the classical trigonometric Fourier basis are given by Efromovich and Pinsker (1996). Our approach is to eploit the heteroscedasticity by using variable bandwidths in local linear regression. Müller and Stadtmüller (1987) discussed variable bandwidth kernel estimators of regression curves. Fan and Gijbels (1992, 1995, 1996) discussed the local linear estimator with variable bandwidth for nonparametric regression models with a single covariate. In this paper, we etend these papers by presenting a local linear estimator with variable bandwidth for nonparametric multiple regression models. We demonstrate that the local linear estimator has optimal conditional mean squared error when Ye, Hyndman and Li: 23 January

3 its variable bandwidth is a function of the density of the eplanatory variables and conditional variances. Numerical simulation shows that the local linear estimator with this variable bandwidth has better goodness of fit than the local linear estimator with constant bandwidth for the heteroscedastic models. 2 Local linear regression with a variable bandwidth Suppose we have a univariate response variable Y and a d-dimensional set of covariates X, and we observe the random vectors (X 1, Y 1 ),, (X n, Y n ) which are independent and identically distributed. It is assumed that each variable in X has been scaled so they have similar measures of spread. Our aim is to estimate the regression function = E(Y X = ). We can regard the data as being generated from the model Y = m(x) + u, (1) where E(u X) = 0, Var(u X = ) = σ 2 () and the marginal density of X is denoted by f(). We assume the second-order derivatives of are continuous, f() is bounded above 0 and σ 2 () is continuous and bounded. Let K be a d-variate kernel which is symmetric, nonnegative, compactly supported, K(u) du = 1 and uu T K(u) du = µ 2 (K)I where µ 2 (K) 0 and I is the d d identity matri. In addition, all odd-order moments of K vanish, that is, u l 1 1 u l d d K(u) du = 0 for all nonnegative integers l 1,, l d such that their sum is odd. Let K h (u) = K(u/h). Then the local linear estimator of with variable bandwidth is ˆm n (, h n, α) = e T 1 (X T W,α X ) 1 X T W,α Y, (2) Ye, Hyndman and Li: 23 January

4 where h n = cn 1/(d+4), c is a constant that depends only on K, α() is the variable bandwidth function, e T 1 = (1, 0,, 0), X = (X,1,, X,n ) T, X,i = (1, (X i )) T and W,α = ( ) diag K hn α(x 1 )(X 1 ),, K hn α(x n )(X n ). We assume α() is continuously differentiable. We now state our main result. Theorem 1 Let be a fied point in the interior of { f() > 0}, H m () = let s(h m ()) be the sum of the elements of H m (). Then ( 2 i j )d d and 1 E [ ˆm n (, h n, α) X 1,, X n ] = 0.5h 2 nµ 2 (K)α 2 ()s(h m ()) + o p (h 2 n); 2 Var [ ˆm n (, h n, α) X 1,, X n ] = n 1 h d n R(K)α d ()σ 2 ()f 1 () + o p (n 1 h d n ), where R(K) = K 2 (u)du; and 3 n 2/(d+4) [ ˆm n (, h n, α) ] d N(0.5c 2 µ 2 (K)α 2 ()s(h m ()), c d R(K)α d ()σ 2 ()f 1 ()). When α() = 1, results 1 and 2 coincide with Theorem 2.1 of Ruppert and Wand (1994). By result 2 and the law of large numbers, we find that ˆm n (, h n, α) is consistent. From result 3 we know that the rate of convergence of ˆm n (, h n, α) in interior points is O(n 2/(d+4) ) which, according to Stone (1980, 1982), is the optimal rate of convergence for estimating nonparametric function. 3 Using heteroscedasticity to improve local linear regression Although Fan and Gijbels (1992) and Ruppert and Wand (1994) discuss the local linear estimator of, nobody has previously developed an improved estimator using the information of heteroscedasticity. We now show how this can be achieved. Ye, Hyndman and Li: 23 January

5 Using Theorem 1, we can give an epression for the conditional mean squared error of the local linear estimator with variable bandwidth. } MSE = E {[ ˆm n (, h n, α) ] 2 X 1,, X n = 1 4 [h nα()] 4 µ 2 2(K)s 2 (H m ()) + R(K)σ2 () n[h n α()] d f() + o p(h 2 n) + o p (n 1 h d n ). (3) Minimizing MSE, ignoring the higher order terms, we obtain the optimal variable bandwidth ( α opt () = c 1 dr(k)σ 2 ) 1/(d+4) () µ 2. 2 (K)f()s2 (H m ()) Note that the constant c will cancel out in the bandwidth epression h n α opt (). Therefore, { } without loss of generality, we can set c = dr(k)µ 2 2 1/(d+4) (K) which simplifies the above epression to ( σ 2 ) 1/(d+4) () α opt () = f()s 2. (4) (H m ()) To apply this new estimator, we need to replace f(), H m () and σ 2 () with estimators. We can use the kernel estimator of f() and the local polynomial estimator of H m (). To estimate σ 2 () = E(u 2 X = ), we first obtain the estimator of u, û i = Y i Ŷi, where Ŷ i = ˆm n (X i, h n, 1). Then we estimate σ 2 () using local polynomial regression with the model û 2 i = σ2 (X i ) + v i where v i are iid with zero mean. In our eamples, we measure relative goodness of fit by the mean of standard squared errors: ( n ) MSSE = n 1 1/2 [ ˆm n (X i, h n, α) m(x i )] 2. (5) Ye, Hyndman and Li: 23 January

6 4 Numerical studies with univariate regression This section eamines the performance of the proposed variable bandwidth method via several data sets of univariate regression, generated from known functions. Also we compare it with the performance of the constant bandwidth method. As the true regression function is known in each case, the performances of the variable and constant bandwidths are measured by MSSE. We consider five simulated models as following subsection. In each of these models Y = m(x)+ σ(x)u with u N(0, 1) and the covariate X has a Uniform ( 2, 2) distribution. 4.1 True regression models Model A: m A () = 2 +, σ 2 A() = Model B: m B () = (1 + ) sin(1.5), σ 2 B() = Model C: m C () = + 2 ep( 2 2 ), σ 2 C() = 16( )I ( 2 >0.01) Model D: m D () = sin(2) + 2 ep( 2 2 ), Ye, Hyndman and Li: 23 January

7 σ 2 D() = 16( )I ( 2 >0.01) Model E: m E () = ep( ( + 1) 2 ) + 2 ep( 2 2 ), σ 2 E() = 32( )I ( 2 >0.01) Bandwidth selectors For the constant bandwidth method, we use direct plug-in methodology to select the bandwidth of a local linear Gaussian kernel regression estimate, as described by Ruppert, Sheather and Wand (1995). For the variable bandwidth method, we use direct plug-in methodology to choose the variable bandwidth. First, we use direct plug-in methodology to select the bandwidth of a kernel density estimate for f(). Second, we estimate σ 2 () = E(u 2 X = ) by using local linear or kernel regression with the model û 2 i = σ2 (X i ) + v i where û i = Y i ˆm n (X i, ĥn, 1),v i are iid with zero mean and ĥn is chosen by the direct plug-in methodology. Third, we use three methods to estimate. For the first method, we assume that = α 1 + α 2 + α α α 5 4, and then = 2α 3 + 6α α 5 2. For the second method, we use revised rule of thumb for bandwidth selection (J. Fan and I. Gijbels, 1996) by replacing the rule of thumb for bandwidth selector (the formula (4.2) in their book) with the ideal choice of bandwidth (the formula (3.20) in their book). For the third method, we average the first and second estimators of in previous methods. Finally, we Ye, Hyndman and Li: 23 January

8 have the direct plug-in bandwidth ( ˆσ 2 ) 1/5 () ĥ() = 2n π ˆf() ˆ m 2. () 4.3 Simulated results We draw 1000 random samples of size 200 from each model for each method. Table 1 presents a summary of the outputs of the variable and constant bandwidth methods. It shows that the variable bandwidth method has less mean of standard squared errors than the constant bandwidth method. Among 1000 samples, the percentages that the variable bandwidth method is better than the constant bandwidth method are totally more than half. We plot the true regression functions (the solid line) and four typical estimated curves in Figure 1 and 2. These correspond to the 10 th, the 30 th, the 70 th, the 90 th percentile. The dashed line is for the constant bandwidth method and the dotted line is for the variable bandwidth method. We find out that for the same percentile, the dotted line (the variable bandwidth method) is almost closer to the solid line (the true regression functions) than the dashed line ( the constant bandwidth method). Therefore, we have the conclusion that for heteroscedastic models, the local linear estimator with variable bandwidth has better goodness of fit than the local linear estimator with constant bandwidth. 5 Numerical studies with bivariate regression This section eamines the performance of the proposed variable bandwidth method via several data sets of bivariate regression, generated from known functions and compares it with the performance of the constant bandwidth method. We consider four simulated models as following subsection. In each of these models Y = m(x 1, X 2 ) + σ(x 1 )u with u N(0, 1) and the covariate X 1 and X 2 are independent and have a Uniform ( 2, 2) distribution. We draw 200 random samples of size 400 from each model. Ye, Hyndman and Li: 23 January

9 5.1 True regression models Model A: m A ( 1, 2 ) = 1 2, σ 2 A( 1, 2 ) = ( )I ( 2 1 >0.04) Model B: m B ( 1, 2 ) = 1 ep( 2 2 2), σ 2 B( 1, 2 ) = 2.5( )I ( 2 1 >0.04) Model C: m C ( 1, 2 ) = sin(1.5 2 ), σ 2 C( 1, 2 ) = ( )I ( 2 1 >0.04) Model D: m D ( 1, 2 ) = sin( ) + 2 ep( 2 2 2), σ 2 D( 1, 2 ) = 3( )I ( 2 1 >0.04) Bandwidth selectors To estimate the second derivative of m( 1, 2 ), we assume that m( 1, 2 ) = α 1 + α α α α α α α α Ye, Hyndman and Li: 23 January

10 +α α α α α α , and then we can obtain the estimators of α. Hence, we easily get the estimators for the second derivative of m( 1, 2 ) by using = 2α 4 + 6α α α α α , = α 5 + 2α α α α α , = 2α 6 + 2α α α α α For the constant bandwidth method, we use direct plug-in methodology to select the bandwidth of a local linear Gaussian kernel regression estimate. To estimate σ 2, we first obtain the estimator of u, û i = Y i Ŷi, where Ŷi = ˆm n (X i, ĥn, 1) and ĥn = min(ĥ1, ĥ2) and ĥ1, ĥ2 are chosen respectively by the direct plug-in methodology for Y = m 1 (X 1 ) + u 1 and Y = m 2 (X 2 ) + u 2. Then ˆσ 2 = n 1 n û 2 i. Finally, we have the direct plug-in bandwidth ( ˆσ 2 ) 1/6 ĥ = π. n s 2 (Ĥm(X i )) For the variable bandwidth method, we also use direct plug-in methodology to choose the variable bandwidth. First, we use direct plug-in methodology to select the bandwidth of a kernel density estimate for X 1 and X 2 respectively, as described by Wand and Jones (1995). Second, we estimate σ 2 ( 1 ) using local linear regression with the model û 2 i = σ2 (X 1i ) + v i, where v i are iid with zero mean. Finally, we have the direct plug-in bandwidth ( ˆσ 2 ) 1/6 () ĥ() = nπ ˆf()s. 2 (Ĥ) Ye, Hyndman and Li: 23 January

11 5.3 Simulated results We draw 200 random samples of size 200 from each model. Table 2 presents a summary of the outputs of the variable and constant bandwidth methods. It shows that the variable bandwidth method has less mean of standard squared errors than the constant bandwidth method. Among 200 samples, the percentages that the variable bandwidth method is better than the constant bandwidth method are totally more than half. We plot the true regression functions with one fied variable (the solid line) and their four typical estimated curves in Figure 3-6. These correspond to the 10 th, the 30 th, the 70 th, the 90 th percentile. The dashed line is for the constant bandwidth method and the dotted line is for the variable bandwidth method. We find out that for the same percentile, the dotted line (the variable bandwidth method) is almost closer to the solid line (the true regression functions) than the dashed line ( the constant bandwidth method). Therefore, we have the conclusion that for heteroscedastic models, the local linear estimator with variable bandwidth has better goodness of fit than the local linear estimator with constant bandwidth. 6 Summary We have presented a local linear nonparametric estimator with variable bandwidth for multivariate regression models. We have shown that the estimator is consistent and asymptotically normal in the interior of the sample space. We have also shown that its convergence rate is optimal for nonparametric regression (Stone, 1980, 1982). By minimizing the conditional mean squared error of the estimator, we have derived the optimal variable bandwidth as a function of the density of the eplanatory variables and the conditional variance. We have also provided a plug-in algorithm for computing the estimator. Numerical simulation shows that our local linear estimator with variable bandwidth has better goodness of fit than the local linear estimator with constant bandwidth for heteroscedastic models. Ye, Hyndman and Li: 23 January

12 Acknowledgements We thank X. Zhang for his helpful comments. References Dette, H., Munk, A., Testing heteroscedasticity in nonparametric regression. Journal of the Royal Statistical Society. Series B, 60, Part4, Efromovich, S., and Pinsker, M., Sharp-optimal and adaptive estimation for heteroscedastic nonparametric regression. Statistica Sinica 6, Eubank, R.L., Thomas, W., Detecting heteroscedasticity in nonparametric regression. Journal of the Royal Statistical Society. Series B, 55(1), Fan, J., Gijbels, I., Variable bandwidth and local linear regression smoothers. The Annals of Statistics, 20(4), Fan, J., Gijbels, I., Data-driven bandwidth selection in local polynomial fitting: variable bandwidth and spatial adaptation. Journal of the Royal Statistical Society. Series B, 57(2), Fan, J., Gijbels, I., Local polynomial modelling and its applications. Chapman and Hall. Härdle, W., Applied nonparametric regression. Cambridge University Press. Müller, H.G., Stadtmüller, U., Variable bandwidth kernel estimators of regression curves. The Annals of Statistics, 15(1), Opsomer, J., Yang, Y., Wang Y., Nonparametric regression with correlated errors. Statistical Science, 16(2), Pagan, A., and Ullah, A., Nonparametric econometrics. Cambridge University Press. Ruppert, D., Wand, M.P., Multivariate locally weighted least squares regression. The Annals of Statistics, 22(3), Ruppert, D., Wand, M.P., Ulla, H., Hossjer, O., Local polynomial variance-function Ye, Hyndman and Li: 23 January

13 estimation. Technometrics, 39, Stone, C.J., Optimal convergence rates for nonparametric estimators. The Annals of Statistics, 8, Stone, C.J., Optimal global rates of convergence for nonparametric regression. The Annals Statist., 10, White, H., Asymptotic theory for econometricians. Academic Press. Xia, Y., Tong, H., Li, W.K., Zhu, L.X., An adaptive estimation of dimension reduction space. Journal of the Royal Statistical Society. Series B, 64, Part 3, Yatchew, A., Nonparametric regression techniques in economics. Journal of Economic Literature, 34, June, Ye, Hyndman and Li: 23 January

14 Appendi A: Proof of Theorem 1 Note that e T 1 (X T W,α X ) 1 X T W,α X = e T 1 D m () =, (6) D m () where D m () = [ 1 ],, T d. Therefore, ˆm n (, h n, α) = e T 1 (X T W,α X ) 1 X T W,α [0.5Q m () + U], (7) where Q m () = [Q m,1 (),, Q m,n ()] T, Q m,i () = (X i ) T H m (z i (, X i ))(X i ), ( H m () = 2 i j )d d, z i(, X i ) X i and U = (u 1,, u n ) T. We can deduce that {z i (, X i )} n are independent because {X i} n obvious that are independent. It is n 1 n 1 X T W,α X = n K hnα(xi )(X i ) n K hn α(x i )(X i )(X i ) n K hnα(x i )(X i )(X i ) T n K hn α(x i )(X i )(X i )(X i ) T. (8) We begin with a lemma using the notation of (2) and Theorem 1. Lemma 1 n 1 n n 1 n n 1 n K hn α(x i )(X i ) = f() + o p (1) (9) K hn α(x i )(X i )(X i ) = h 2 nα 3 ()G(α, f, ) + o p (h 2 n1) (10) K hnα(x i )(X i )(X i )(X i ) T = µ 2 (K)h 2 nα 2 ()f()i + o p (h 2 n1) (11) Ye, Hyndman and Li: 23 January

15 where [ ] G(α, f, ) = f() udα T ()uu T D K (u)du + µ 2 (K) df()d α () + α 1 ()D f () supp(k) and 1 is a generic matri having each entry equal to 1, the dimensions of which will be clear from the contet. (X T W,α X ) 1 = f() 1 + o p (1) B(, α) + o p (1) (12) B T (, α) + o p (1) µ 2 (K) 1 h 2 n α 2 ()f() 1 I + o p (h 2 n 1) f()µ 2(K)α 2 ()s(h m ()) n 1 X T W,α Q m () = h 2 n + o p (h 2 n1) (13) 0 (nh d n) 1/2 (n 1 X T W,α u) d where B(, α) = µ 2 (K) 1 f() 2 α()g(α, f, ) T. N(0, R(K)α d ()σ 2 ()f()). (14) 0 We only prove results (10), (13) and (14) as the other results can be proved similarly. Proof of (10) It is easy to show that n 1 n where Ψ = [ ] K hn α(x i )(X i )(X i ) = E K hn α(x 1 )(X 1 )(X 1 ) + O p ( n 1 Ψ), (15) ( Var(K hnα(x 1 )(X 1 )(X 11 1 )),, Var(K hnα(x 1 )(X 1 )(X d1 d ))) T. Because is a fied point in the interior of supp(f) = { f() 0}, we have supp(k) {z : ( + h n α()z) supp(f)}, provided that the bandwidth h n is small enough. Ye, Hyndman and Li: 23 January

16 Due to the continuity of f, K and α, we have [ ] E K hn α(x 1 )(X 1 )(X 1 ) = h d n α d ()K(h 1 n (α(x 1 )) 1 (X 1 ))(X 1 )f(x 1 )dx 1 supp(f) = (α( + h n Q)) d K(Q(α( + h n Q)) 1 )f( + h n Q)h n QdQ Ω n = h 2 nα() 3 G(α, f, ) + o(h 2 n1), (16) where Ω n = {Q : + h n Q supp(f)}. It is easy to see [ ] Var K hn α(x 1 )(X 1 )(X 1 ) { [ ] [ ] } T = E K hn α(x 1 )(X 1 )(X 1 ) K hn α(x 1 )(X 1 )(X 1 ) { [ ]} { [ T E K hn α(x 1 )(X 1 )(X 1 ) E K hn α(x 1 )(X 1 )(X 1 )]}. (17) Again by the continuity of f, K and α, we have { [ ] [ ] } T E K hn α(x 1 )(X 1 )(X 1 ) K hn α(x 1 )(X 1 )(X 1 ) = E [K ] hn α(x 1 )(X 1 ) 2 (X 1 )(X 1 ) T = [ 2 h d n (α()) d K(h 1 n (α(x 1 )) 1 (X 1 ))] (X1 )(X 1 ) T f(x 1 )dx 1 supp(f) = h d+2 n (α( + h n Q)) d K(Q(α( + h n Q)) 1 ) 2 f( + h n Q)QQ T dq Ω n = h d+2 n (α()) d K(Q(α()) 1 ) 2 f()qq T dq + O(h d+2 n 1) = O(h d+2 n 1). Ω n (18) Therefore we have O p ( n 1 Ψ) = o p (h 2 n1). (19) Then (10) follows from (15) (19). Ye, Hyndman and Li: 23 January

17 Proof of (13) It is straightforward to show that n 1 X T W,α Q m () n 1 n K hn α(x i )(X i )(X i ) T H m (z i (, X i ))(X i ) = n 1. (20) n K hn α(x i )(X i )(X i ) T H m (z i (, X i ))(X i )(X i ) It is easy to prove that n n 1 n n 1 K hn α(x i )(X i )(X i ) T H m (z i (, X i ))(X i ) (21) = h 2 nf()µ 2 (K)(α()) 2 s(h m ()) + o p (h 2 n1), K hn α(x i )(X i )(X i ) T H m (z i (, X i ))(X i )(X i ) = O p (h 3 n1). (22) Therefore (13) holds. Proof of (14) It is obvious that [ ] [ ] E n 1 X T W,α u = E n 1 X T W,α u = 0, n 1 n K n 1 X T hn α(x i )(X i )u i W,α u = n 1. (23) n K hnα(xi )(X i )(X i )u i By the continuity of f, K, σ 2 and α, we have Var [ n 1 n ] ] K hn α(x i )(X i )u i = n 1 Var [K hn α(x 1 )(X 1 )u 1 [ 2 = n 1 h d n (α()) d K(h 1 n (α(x 1 )) 1 (X 1 ))] σ 2 (X 1 )f(x 1 )dx 1 supp(f) = n 1 h d n ((α( + h n Q)) d K(Q(α( + h n Q)) 1 )) 2 σ 2 ( + h n Q)f( + h n Q)dQ Ω n = n 1 h d n ((α()) d K(Q(α()) 1 )) 2 σ 2 ()f()dq + o(n 1 h d n ) Ω n Ye, Hyndman and Li: 23 January

18 = n 1 h d n R(K)(α()) d σ 2 ()f() + o(n 1 h d n ), (24) [ n ] Var n 1 K hnα(xi )(X i )(X i )u i = o(n 1 h d+2 n 1). (25) Then (14) holds. Proof of Theorem 1 Proof of Theorem 1(1) By (12) and (13), we have E [ ˆm n (, h n, α) X 1,, X n ] = 0.5e T 1 (X T W,α X ) 1 X T W,α Q m () = 0.5h 2 nµ 2 (K)(α()) 2 s(h m ()) + o p (h 2 n). (26) Therefore Theorem 1(1) holds. Proof of Theorem 1(2) Denote V = diag { σ 2 (X 1 ),, σ 2 (X n ) }. It is obvious that Var [ ˆm n (, h n, α) X 1,, X n ] = e T 1 (X T W,α X ) 1 X T W,α V W,α X (X T W,α X ) 1 e 1, (27) and a 11(, h n, α) (a 21 (, h n, α)) T n 1 X T W,α V W,α X =, (28) a 21 (, h n, α) a 22 (, h n, α) Ye, Hyndman and Li: 23 January

19 where n a 11 (, h n, α) = n 1 (K hn α(x i )(X i )) 2 σ 2 (X i ) n a 21 (, h n, α) = n 1 (K hn α(x i )(X i )) 2 (X i )σ 2 (X i ) n a 22 (, h n, α) = n 1 (K hnα(xi )(X i )) 2 (X i )(X i ) T σ 2 (X i ). It is easy to prove that n n 1 n n 1 n n 1 (K hn α(x i )(X i )) 2 σ 2 (X i ) = h d R(K)(α()) d σ 2 ()f()o p (h d (K hn α(x i )(X i )) 2 (X i ) T σ 2 (X i ) = O p (h d+1 n 1) n (K hn α(x i )(X i )) 2 (X i )(X i ) T σ 2 (X i ) = O p (h d+2 n 1). (29) n ) By (27) (29) and (12), we have Var [ ˆm n (, h n, α) X 1,, X n ] = n 1 h d n R(K)(α()) d σ 2 ()f() 1 + o p (n 1 h d n ). (30) Therefore Theorem 1(2) holds. Proof of Theorem 1(3) By (13) and (14) and the central limit theorem, we have n 2/(d+4) n 1 X T d W,α [0.5Q m () + u] N(0.5c2 µ 2 (K)(α()) 2 f()s(h m ()), c d R(K)(α()) d σ 2 ()f()). (31) 0 Applying White (1984, Proposition 2.26) and (12), we can easily deduce Theorem 1(3). Ye, Hyndman and Li: 23 January

20 Table 1: The percentage of 1000 samples (in which the variable bandwidth method is better than the constant bandwidth method) and the mean of standard squared errors of two methods. Model and Percentage Mean of standard squared errors the method estimating Constant bandwidth Variable bandwidth Model A, method Model A, method Model A, method Model B, method Model B, method Model B, method Model C, method Model C, method Model C, method Model D, method Model D, method Model D, method Model E, method Model E, method Model E, method Table 2: The percentage of 200 samples (in which the variable bandwidth method is better than the constant bandwidth method) and the mean of standard squared errors of two methods. Model Percentage Mean of standard squared errors Constant bandwidth Variable bandwidth Model A Model B Model C Model D Ye, Hyndman and Li: 23 January

21 Figure 1: The results for the simulated univariate regression data of model A, B and C. The true regression functions (the solid line) and four typical estimated curves are presented. These correspond to the 10 th, the 30 th, the 70 th, the 90 th percentile. The dashed line is for the constant bandwidth method and the dotted line is for the variable bandwidth method. Model A, method 1 Model A, method 2 Model A, method Model B, method 1 Model B, method 2 Model B, method Model C, method 1 Model C, method 2 Model C, method Ye, Hyndman and Li: 23 January

22 Figure 2: The results for the simulated univariate regression data of model D and E. The true regression functions (the solid line) and four typical estimated curves are presented. These correspond to the 10 th, the 30 th, the 70 th, the 90 th percentile. The dashed line is for the constant bandwidth method and the dotted line is for the variable bandwidth method. Model D, method 1 Model D, method 2 Model D, method Model E, method 1 Model E, method 2 Model E, method Ye, Hyndman and Li: 23 January

23 Figure 3: The results for the simulated bivariate data of model A. The true regression functions (the solid line) and four typical estimated curves are presented. These correspond to the 10 th, the 30 th, the 70 th, the 90 th percentile. The dashed line is for the constant bandwidth method and the dotted line is for the variable bandwidth method. Model A, _2=1 Model A, _1=1 m(_1,1) m(1,_2) _1 _2 Model A, _2=0 Model A, _1=0 m(_1,0) m(0,_2) _1 _2 Model A, _2= 1 Model A, _1= 1 m(_1, 1) m( 1,_2) _1 _2 Ye, Hyndman and Li: 23 January

24 Figure 4: The results for the simulated bivariate data of model B. The true regression functions (the solid line) and four typical estimated curves are presented. These correspond to the 10 th, the 30 th, the 70 th, the 90 th percentile. The dashed line is for the constant bandwidth method and the dotted line is for the variable bandwidth method. Model B, _2=1 Model B, _1=1 m(_1,1) m(1,_2) _1 _2 Model B, _2=0 Model B, _1=0 m(_1,0) m(0,_2) _1 _2 Model B, _2= 1 Model B, _1= 1 m(_1, 1) m( 1,_2) _1 _2 Ye, Hyndman and Li: 23 January

25 Figure 5: The results for the simulated bivariate data of model C. The true regression functions (the solid line) and four typical estimated curves are presented. These correspond to the 10 th, the 30 th, the 70 th, the 90 th percentile. The dashed line is for the constant bandwidth method and the dotted line is for the variable bandwidth method. Model C, _2=1 Model C, _1=1 m(_1,1) m(1,_2) _1 _2 Model C, _2=0 Model C, _1=0 m(_1,0) m(0,_2) _1 _2 Model C, _2= 1 Model C, _1= 1 m(_1, 1) m( 1,_2) _1 _2 Ye, Hyndman and Li: 23 January

26 Figure 6: The results for the simulated bivariate data of model D. The true regression functions (the solid line) and four typical estimated curves are presented. These correspond to the 10 th, the 30 th, the 70 th, the 90 th percentile. The dashed line is for the constant bandwidth method and the dotted line is for the variable bandwidth method. Model D, _2=1 Model D, _1=1 m(_1,1) m(1,_2) _1 _2 Model D, _2=0 Model D, _1=0 m(_1,0) m(0,_2) _1 _2 Model D, _2= 1 Model D, _1= 1 m(_1, 1) m( 1,_2) _1 _2 Ye, Hyndman and Li: 23 January