function [FPE,AIC,BIC,SBC,MDL,CATcrit,PHI,optFPE,optAIC,optBIC,optSBC,optMDL,optCAT,optPHI,p,C]=selmo(e,NC);% Model order selection of an autoregrssive model% [FPE,AIC,BIC,SBC,MDL,CAT,PHI,optFPE,optAIC,optBIC,optSBC,optMDL,optCAT,optPHI]=selmo(E,N);%% E	Error function E(p)% N	length of the data set, that was used for calculating E(p)% show  optional; if given the parameters are shown%% FPE	Final Prediction Error (Kay 1987, Wei 1990, Priestley 1981  -> Akaike 1969)% AIC	Akaike Information Criterion (Marple 1987, Wei 1990, Priestley 1981 -> Akaike 1974)% BIC	Bayesian Akaike Information Criterion (Wei 1990, Priestley 1981 -> Akaike 1978,1979)% CAT	Parzen's CAT Criterion (Wei 1994 -> Parzen 1974)% MDL	Minimal Description length Criterion (Marple 1987 -> Rissanen 1978,83)% SBC	Schwartz's Bayesian Criterion (Wei 1994; Schwartz 1978)% PHI	Phi criterion (Pukkila et al. 1988, Hannan 1980 -> Hannan & Quinn, 1979)% HAR	Haring G. (1975)% JEW	Jenkins and Watts (1968)%% optFPE 	order where FPE is minimal% optAIC 	order where AIC is minimal% optBIC 	order where BIC is minimal% optSBC 	order where SBC is minimal% optMDL 	order where MDL is minimal% optCAT 	order where CAT is minimal% optPHI 	order where PHI is minimal%% usually is % AIC > FPE > *MDL* > PHI > SBC > CAT ~ BIC%% REFERENCES:%  P.J. Brockwell and R.A. Davis "Time Series: Theory and Methods", 2nd ed. Springer, 1991.%  S. Haykin "Adaptive Filter Theory" 3ed. Prentice Hall, 1996.%  M.B. Priestley "Spectral Analysis and Time Series" Academic Press, 1981. %  C.E. Shannon and W. Weaver "The mathematical theory of communication" University of Illinois Press, Urbana 1949 (reprint 1963).%  W.S. Wei "Time Series Analysis" Addison Wesley, 1990.%  Jenkins G.M. Watts D.G "Spectral Analysis and its applications", Holden-Day, 1968.%  G. Haring  "躡er die Wahl der optimalen Modellordnung bei der Darstellung von station鋜en Zeitreihen mittels Autoregressivmodell als Basis der Analyse von EEG - Biosignalen mit Hilfe eines Digitalrechners", Habilitationschrift - Technische Universit鋞 Graz, Austria, 1975.%                  (1)"About selecting the optimal model at the representation of stationary time series by means of an autoregressive model as basis of the analysis of EEG - biosignals by means of a digital computer)"%%% (1) engl. translation of the titel by A. Schloegl%	Version 2.99b%	last revision 01.10.2002%	Copyright (C) 1997-2002 by Alois Schloegl <a.schloegl@ieee.org>% This library is free software; you can redistribute it and/or% modify it under the terms of the GNU Library General Public% License as published by the Free Software Foundation; either% Version 2 of the License, or (at your option) any later version.%% This library is distributed in the hope that it will be useful,% but WITHOUT ANY WARRANTY; without even the implied warranty of% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU% Library General Public License for more details.%% You should have received a copy of the GNU Library General Public% License along with this library; if not, write to the% Free Software Foundation, Inc., 59 Temple Place - Suite 330,% Boston, MA  02111-1307, USA.[lr,lc]=size(e);if (lr>1) & (lc>1),         p=zeros(lr+1,9)+NaN;else        p=zeros(1,9)+NaN;end;if nargin<2         NC=lc*ones(lr,1); 	NC=(lc-sum(isnan(e)')')*(NC<lc) + NC.*(NC>=lc); % first part %end;% Pmax=min([100 N/3]); end;	%if NC<lc N=lc; end;         %NC=(lc-sum(isnan(e)')')*(NC<lc) + NC.*(NC>=lc); % first part else        % NC=NC;end;M=lc-1;m=0:M;e = e./e(:,ones(1,lc));

for k=0:lr,        if k>0, %                 E=e(k,:);                N=NC(k);        elseif lr>1                tmp = e;%(NC>0,:);                tmp(isnan(tmp)) = 0;                E = sum(tmp.*(NC*ones(1,lc)))/sum(NC); % weighted average, weigths correspond to number of valid (not missing) values                 N = sum(NC)./sum(NC>0); % corresponding number of values,         else                E = e;                N = NC;        end;FPE = E.*(N+m)./(N-m);	%OK		optFPE=find(FPE==min(FPE))-1;	%optimal order        if isempty(optFPE), optFPE=NaN; end;AIC = N*log(E)+2*m; 	%OK	optAIC=find(AIC==min(AIC))-1;	%optimal order        if isempty(optAIC), optAIC=NaN; end;AIC4=N*log(E)+4*m;	%OK	optAIC4=find(AIC4==min(AIC4))-1;	%optimal order        if isempty(optAIC4), optAIC4=NaN; end;m=1:M;BIC=[ N*log(E(1)) N*log(E(m+1)) - (N-m).*log(1-m/N) + m*log(N) + m.*log(((E(1)./E(m+1))-1)./m)];%BIC=[ N*log(E(1)) N*log(E(m+1)) - m + m*log(N) + m.*log(((E(1)./E(m+1))-1)./m)];%m=0:M; BIC=N*log(E)+m*log(N);          % Hannan, 1980 -> Akaike, 1977 and Rissanen 1978        optBIC=find(BIC==min(BIC))-1;	%optimal order        if isempty(optBIC), optBIC=NaN; end;        HAR(2:lc)=-(N-m).*log((N-m).*E(m+1)./(N-m+1)./E(m));                 HAR(1)=HAR(2);	optHAR=min(find(HAR<=(min(HAR)+0.2)))-1;	%optimal order%	optHAR=find(HAR==min(HAR))-1;	%optimal order        if isempty(optHAR), optHAR=NaN; end;        m=0:M;SBC = N*log(E)+m*log(N);	optSBC=find(SBC==min(SBC))-1;	%optimal order        if isempty(optSBC), optSBC=NaN; end;MDL = N*log(E)+log(N)*m;	optMDL=find(MDL==min(MDL))-1;	%optimal order        if isempty(optMDL), optMDL=NaN; end;        m=0:M;%CATcrit= (cumsum(1./E(m+1))/N-1./E(m+1));E1=N*E./(N-m);CATcrit= (cumsum(1./E1(m+1))/N-1./E1(m+1));		optCAT=find(CATcrit==min(CATcrit))-1;	%optimal order        if isempty(optCAT), optCAT=NaN; end;PHI = N*log(E)+2*log(log(N))*m;	optPHI=find(PHI==min(PHI))-1;	%optimal order        if isempty(optPHI), optPHI=NaN; end;        JEW = E.*(N-m)./(N-2*m-1);	% Jenkins-Watt	optJEW=find(JEW==min(JEW))-1;	%optimal order        if isempty(optJEW), optJEW=NaN; end;        % in case more than 1 minimum is found, the smaller model order is returned;p(k+1,:) = [optFPE(1), optAIC(1), optBIC(1), optSBC(1), optCAT(1), optMDL(1), optPHI(1), optJEW(1), optHAR(1)];end;
C=[FPE;AIC;BIC;SBC;MDL;CATcrit;PHI;JEW;HAR(:)']';