Complex, |z|.
int.
long.
float.
double.
ResultSet
object.
Complex,
with branch cuts outside the interval [-1,1] along the
real axis.
double.
Complex,
with a branch cut at values less than one along the real axis.
Complex objects, x+y.
Complex and a double, x+y.
double and a Complex, x+y.
Complex arrays, a + b.
A, B of type
ComplexSparseMatrix,
Physical objects.
A, B of type
SparseMatrix,
ChartNode.
ControlLimit,
adds it to the control chart,
and returns the newly created object.
Perceptron with this Layer.
ControlLimit,
adds it to the control chart,
and returns the newly created object.
(adjusted 
)
criterion regression.
examineStep to indicate examining after
a successful step
examineStep to indicate examining after
an unsuccessful step
ResultSet object, just
after the last row.
where A is the matrix of coefficients to solve
and p and z are arrays of length n, the order of matrix A.
) between the lines through the
origin defined by the vectors distance method.
Text object at the specific x,y location
in chart coordinates.
String at the specific x,y location
in chart coordinates.
Image object centered at an x,y location
in chart coordinates.
ANOVAFactorial.
maxLag.
ARAutoUnivariate constructor.
Complex, in radians,
with a branch cut along the negative real axis.
ARMA.
IllConditionedException with the
specified detail message.
IllConditionedException with the
specified detail message.
IncreaseErrRelException with the
specified detail message.
IncreaseErrRelException with the
specified detail message.
MatrixSingularException with the
specified detail message.
MatrixSingularException with the
specified detail message.
NewInitialGuessException with the
specified detail message.
NewInitialGuessException with the
specified detail message.
TooManyCallsException with the specified
detail message.
TooManyCallsException with the specified
detail message.
TooManyFcnEvalException with the
specified detail message.
TooManyFcnEvalException with the
specified detail message.
TooManyITNException with the specified
detail message.
TooManyJacobianEvalException with the
specified detail message.
TooManyJacobianEvalException with the
specified detail message.
ARMAEstimateMissing.
ARMAMaxLikelihood.
InitialMAException with the
specified detail message.
InitialMAException with the
specified detail message.
NonInvertible exception with the
specified detail message.
NonInvertibleException exception with the
specified detail message.
NonStationary exception with the
specified detail message.
NonStationary exception with the
specified detail message.
ARMAOutlierIdentification.
ARSeasonalFit.
nkeys.
nkeys and
returns the permutation vector.
Complex,
with branch cuts outside the interval [-1,1] along the
real axis.
double.
Complex,
with branch cuts outside the interval [-i,i].
Complex,
with branch cuts outside the interval [-i,i] along the
imaginary axis.
double.
Complex,
with branch cuts outside the interval [-1,1] on the real axis.
model and produces forecasts that incorporate the effects of
outliers whose effects persist beyond the end of the series.AutoARIMA.
NoAcceptableModelFoundException exception
with the specified detail message.
NoAcceptableModelFoundException exception
with the specified detail message.
NonPosVariancesException with the
specified detail message.
NonPosVariancesException with the
specified detail message.
Axis node.
AxisXY.
AxisXYZ.
![c_{pk}=minleft[frac{mathrm{USL}-bar{x}}{3sigma},frac{bar{x}-mathrm{LSL}}{3sigma}right]](eqn_0036.png){kind=small}
![c_{pk}=minleft[frac{mathrm{USL}-bar{x}}{3sigma},frac{bar{x}-mathrm{LSL}}{3sigma}right]](eqn_0023.png){kind=small}
.
.
models to the outlier free
time series.
models to the outlier free
time series.
model to the outlier free time series.
weights for an outlier contaminated time series whose underlying outlier free
series obeys a general seasonal or non-seasonal ARMA model.
condition number.
condition number
of a matrix.
the mean of the ranges of
n samples from a Gaussian distribution.
the standard deviation of the ranges of
n samples from a Gaussian distribution.
where 
at the given point.
and its derivative 
.
at
origins 
.
weights for the given outlier contaminated time series.
when the Gram-Schmidt implementation is used.
.
bars.
bars.
values.
values.
.
.
, where p is the AR order, AIC is Akaike's Information
Criterion, and L is the likelihood function evaluated for the
optimum autoregressive model.
, where
is equal to the likelihood function evaluated using the
final parameter estimates.
,
the maximum difference between the theoretical and empirical CDF's.
,
the maximum difference between the theoretical and empirical CDF's.
allowed.
,
the minimum difference between the theoretical and empirical CDF's.
,
the minimum difference between the theoretical and empirical CDF's.
,
the number of consecutive periods that the cusums
have been nonzero.
,
the number of consecutive periods that the cusums
have been nonzero.
made.
weights for the
detected outliers.
of the
optimum model.
weights of the infinite order moving average
form of the model.
weights of the infinite order
moving average form of the model.
vs.
.
.
.
.
.
.
).
norm)
distance method.
coefficient at the given point.
norm) distance method.
norm).
.
criterion regression.
.
.
.
.
coefficient at the given point.
.
.
.
.
.
.
in the Gram-Schmidt implementation.
with
m components in n independent variables.
where M is the
preconditioning matrix and r and z are arrays of length 
where M is the
preconditioning matrix and r and z are arrays of length 
function, also known as the
trigamma function.
criterion regression.
, where d is the dimension.
.
.
.
.
to be added
to the linear response.
and its derivative 
used in the FeynmanKac PDE.
.
.
, on the variables.
allowed.
criterion is
used.
.
.
.
.
in the Feynman Kac equation.
, on the variables.
coefficient at the given point.
at the given point.
.
.
using
a conjugate gradient method with or without preconditioning.
using the Generalized
Residual Method.
.
.
.
.
.
.
.
dummies are defined in terms of the indicator variables
so that for balanced data, the usual summation restrictions are
imposed on the regression coefficients.
.
.
,
where 
are the user basis functions
evaluated at index values
is the intercept, 
are the coefficients associated with the basis functions, and is the random
error associated with y.