Keywords - Function Groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

gam

backfit
the estimates for the components of an additive (partial linear) model are calculated. If the local linear smoother is applied, the first derivatives are calculated as well, additionally the second derivatives if the local quadratic smoother is chosen.
DSFM
estimates a dynamic semiparametric factor model from the form: yt = m0(u) + bt1*m1(u) + bt2*m2(u)... btL*mL(u), where m0 to mL are 2-dimensional invariant basis functions on the grid u and bt0=1. bt1 to btL are scalar weights depending on time T. After estimation, the functions m are orthogonalized
DSFM4IV
estimates a dynamic semiparametric factor model from the form: yt = m0(u) + bt1*m1(u) + bt2*m2(u)... btL*mL(u), where m0 to mL are 2-dimensional invariant basis functions on the grid u and bt0=1. bt1 to btL are scalar weights depending on time T. After estimation, the functions m are orthogonalized
fastint
fastint estimates the additive components and their derivatives of an additive model using a modification of the integration estimator plus a one step backfit, see Kim, Linton and Hengartner (1997) and Linton (1996)
gamfit
gamfit provides an interactive tool for fitting additive models
gammain
sets defaults for library gam
gamopt
defines a list with optional parameters in gam quantlets. The list is either created or new options are appended to an existing list. Note that gamopt does accept any values for the parameters without validation.
gamout
auxiliary quantlet, creates a nice output for GAM.
gamtest
auxiliary quantlet, tests all quantlets of gam.lib (listed in See_also)
gintest
estimation of the univariate additive functions in a separable generalized additive model using Nad.Watson, local linear or local quadratic
gintestpl
gintestpl fits an additive generalized partially linear model E[y|x,t] = G(x*b + m(t)). This quantlet offers a convenient interface for GPLM estimation. A preparation of data is performed (inclusive sorting).
interact
interact estimates a model with interaction terms. It is using the marginal integration estimator with a local polynomial smoother. For details see Sperlich, Tjostheim, Yang (1997)
intertest1
intertest1 is testing for interaction of x_1 and x_2 in an additive regression model. It is looking at the interation estimate and using wild bootstrap. For details see Sperlich, Tjostheim, Yang (1997)
intertest2
intertest2 is testing for interaction of x_1 and x_2 in an additive regression model. It is looking at the estimate of the mixed derivative of the joint influence and using wild bootstrap. For details see Sperlich, Tjostheim, Yang (1997)
intest
estimation of the univariate additive functions in a separable additive model using Nadaraya-Watson, local linear or local quadratic estimation.
intest1
estimation of the univariate additive functions in a separable additive model using Nad.Wat.
intest2d
estimation of a bivariate joint influence function and its derivatives in a model with possible interaction. When loc.lin.smoother is chosen you get the function estimate and the first derivatives in the first and second direction, when loc.quadr.smoother is chosen you get the function and the mixe
intestpl
estimation of the univariate additive functions in a separable additive partial linear model using local polynomial estimation
pcad
pcad estimates the additive components, the significant directions and the regression on principal components
sptest
Additive component analysis in additive separable models using wavelet estimation. An additive component can be tested against a given polynomial form with degree p, e.g. when p is set to zero we test for significant influence of that component. The procedure is presented in Haerdle, Sperlich,
TimeVarAddModel2
estimates a dynamic factor model from the form: yt = m0(u) + bt1*m1(u) + bt2*m2(u)... btL*mL(u), where m0 to mL are 2-dimensional invariant basis functions on the grid u and bt0=1. bt1 to btL are scalar weights depending on time T. After estimation, the functions m are orthogonalized under the empi
wavetest
Additive component analysis in additive separable models using wavelet estimation. The first (additive) component is tested against a given polynomial form of degree p, e.g., if p=1 is to test linearity, p=0 is to test for significant influence of the first component at all etc. For details see Hae

Keywords - Function Groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

(C) MD*TECH Method and Data Technologies, 05.02.2006Impressum