583 583
regression procedures. Further discussion about the underlying theory can be

584 584
found in the \scAD\ user's manual, but it is not necessary to understand the

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theory to make use of the procedure.

586
\begin{figure}[h]

586
\begin{figure}[htbp]

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  \centering\hskip1pt\beginpicture

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  \setcoordinatesystem units <.18in,.04in>

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  \setplotarea x from 0 to 16.5, y from 0 to 50

......
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analysis. An alternative approach, which avoids these difficulties, is to employ

676 676
\ADM's robust regression procedure, which removes the undue influence of

677 677
outlying points without the need to expressly remove them from the data.

678
\begin{figure}[h]

678
\begin{figure}[htbp]

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  \centering\hskip1pt\beginpicture

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    \setcoordinatesystem units <.18in,.04in>

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    \setplotarea x from 0 to 16.5, y from 0 to 50

......
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template from a nonlinear regression model to a robust nonlinear regression

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model.

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\begin{figure}[h]

786
\begin{figure}[htbp]

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\centering\hskip1pt\beginpicture

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  \setcoordinatesystem units <.18in,.04in>

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  \setplotarea x from 0 to 16.5, y from 0 to 50

......
826 826
better than least-square estimates in the presence of moderate or large

827 827
outliers. You can lose only a little and you stand to gain a lot by using these

828 828
estimators.

829
\begin{figure}[h]

829
\begin{figure}[htbp]

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\centering\hskip1pt\beginpicture

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  \setcoordinatesystem units <.18in,.04in>

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  \setplotarea x from 0 to 16.5, y from 0 to 50

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concentrations of the reactants are known only approximately.

890 890
See~Table~\ref{tab:runs} for what they are.

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%xx \htmlbegintex

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\begin{table}[h]

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\begin{table}[htbp]

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\begin{center}

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\begin{tabular}{@{\extracolsep{1em}} l c  l}

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 \bf Run 1&$s_1(0)=\theta_5=1\pm0.05$&$s_2(0)=\theta_6=1\pm0.05$\\

......
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produced this way. Also, a plot of $y_i$ versus $x_i$ gives the user an

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indication of what the probability distribution of the parameter looks like.

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(See Figure~\ref{fig:05}.)

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\begin{figure}[h]

2138
\begin{figure}[htbp]

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\centering\hskip1pt\beginpicture

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    \setplotsymbol ({\eightrm .})

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  %\setcoordinatesystem units <.7in,5in> Have to adjust so labels don't overlap.

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the estimates produced by the \textsc{mcmc} method, for different sample sizes

2579 2579
(25,000 and 2,500,000 samples) for the \texttt{catage} example.

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%xx \htmlbeginignore

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\begin{figure}[h]

2581
\begin{figure}[htbp]

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%xx \htmlendignore

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\centering\hskip1pt\beginpicture

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  \setplotsymbol ({\eightrm .})

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standard deviations fixed. The number \texttt{N} should be between~1 and~9. The

2912 2912
smaller the number, the more the correlation is reduced. For this example (see

2913 2913
Figure~\ref{fig:07}), a value of~3 seemed to perform well.

2914
\begin{figure}[h]

2914
\begin{figure}[htbp]

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\includegraphics[height=3.5in, width=\textwidth]{mcrb3-50K.png}

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\emptycaption

2917 2917
\label{fig:07}

......
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The plot of \texttt{qa} and \texttt{qb} demonstrates the extra information about

3788 3788
the probability distribution of the current state contained in in the current

3789 3789
value of~\texttt{y(t)}. (See Figure~\ref{fig:08}.)

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\begin{figure}[h]

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\begin{figure}[htbp]

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\centering\hskip1pt\beginpicture

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  \setplotsymbol ({\eightrm .})

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  \setcoordinatesystem units <3.2in,2.5in>


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