families | R Documentation |
Families known to the package. These functions construct simple family specifications used in specifying aster models. Statistical properties of these families are described.
fam.bernoulli()
fam.poisson()
fam.zero.truncated.poisson()
fam.normal.location.scale()
fam.multinomial(dimension)
dimension |
the dimension (number of categories) for the multinomial distribution. |
Currently implemented families are
"bernoulli"
Bernoulli (binomial with sample size one).
The distribution of any
zero-or-one-valued random variable Y
, which is the canonical
statistic. The mean value parameter is
\mu = E(Y) = \Pr(Y = 1).
The canonical parameter is
\theta = \log(\mu) - \log(1 - \mu)
,
also called logit of \mu
. The cumulant function is
c(\theta) = \log(1 + e^\theta).
This distribution has degenerate limiting distributions. The lower
limit as \theta \to - \infty
is the
distribution concentrated at zero, having cumulant function which
is the constant function everywhere equal to zero. The upper
limit as \theta \to + \infty
is the
distribution concentrated at one, having cumulant function which
is the identity function satisfying
c(\theta) = \theta
for all \theta
.
For predecessor (sample size) n
, the successor is the sum of
n
independent and identically distributed (IID) Bernoulli
random variables, that is,
binomial with sample size n
. The mean value parameter is n
times the mean value parameter for sample size one; the cumulant function
is n
times the cumulant function for sample size one; the canonical
parameter is the same for all sample sizes.
"poisson"
Poisson. The mean value parameter
\mu
is the mean of the Poisson distribution.
The canonical parameter is \theta = \log(\mu)
.
The cumulant function is
c(\theta) = e^\theta.
This distribution has a degenerate limiting distribution. The lower
limit as \theta \to - \infty
is the
distribution concentrated at zero, having cumulant function which
is the constant function everywhere equal to zero. There is no upper
limit because the canonical statistic is unbounded above.
For predecessor (sample size) n
, the successor is the sum of
n
IID Poisson random variables, that is,
Poisson with mean n \mu
. The mean value parameter is n
times the mean value parameter for sample size one; the cumulant function
is n
times the cumulant function for sample size one; the canonical
parameter is the same for all sample sizes.
"zero.truncated.poisson"
Poisson conditioned on being
greater than zero. Let m
be the mean of the corresponding
untruncated Poisson distribution. Then the canonical parameters for both
truncated and untruncated distributions are the same
\theta = \log(m)
.
The mean value parameter for the zero-truncated Poisson distribution is
\mu = \frac{m}{1 - e^{- m}}
and the cumulant function is
c(\theta) = m + \log(1 - e^{- m}),
where m
is as defined above,
so m = e^\theta
.
This distribution has a degenerate limiting distribution. The lower
limit as \theta \to - \infty
is the
distribution concentrated at one, having cumulant function which
is the identity function satisfying
c(\theta) = \theta
for all \theta
.
There is no upper
limit because the canonical statistic is unbounded above.
For predecessor (sample size) n
, the successor is the sum of
n
IID zero-truncated Poisson random variables, which is not
a brand-name distribution. The mean value parameter is n
times the mean value parameter for sample size one; the cumulant function
is n
times the cumulant function for sample size one; the canonical
parameter is the same for all sample sizes.
"normal.location.scale"
The distribution of a normal
random variable X
with unknown mean m
and unknown variance
v
. Thought of as an exponential family, this is
a two-parameter family, hence must have a two-dimensional canonical
statistic Y = (X, X^2)
. The canonical parameter
vector \theta
has components
\theta_1 = \frac{m}{v}
and
\theta_2 = - \frac{1}{2 v}.
The value of \theta_1
is unrestricted, but
\theta_2
must be strictly negative.
The mean value parameter vector \mu
has components
\mu_1 = m = - \frac{\theta_1}{2 \theta_2}
and
\mu_2 = v + m^2 = - \frac{1}{2 \theta_2} + \frac{\theta_1^2}{4 \theta_2^2}.
The cumulant function is
c(\theta) = - \frac{\theta_1^2}{4 \theta_2} + \frac{1}{2} \log\left(- \frac{1}{2 \theta_2}\right).
This distribution has no degenerate limiting distributions, because the canonical statistic is a continuous random vector so the boundary of its support has probability zero.
For predecessor (sample size) n
, the successor is the sum of
n
IID random vectors (X_i, X_i^2)
,
where each X_i
is normal
with mean m
and variance v
, and this is not
a brand-name multivariate distribution (the first component of the sum
is normal, the second component noncentral chi-square, and the
components are not independent).
The mean value parameter vector is n
times the mean value parameter vector for sample size one;
the cumulant function
is n
times the cumulant function for sample size one; the canonical
parameter vector is the same for all sample sizes.
"multinomial"
Multinomial with sample size one.
The distribution of any random vector Y
having all components zero
except for one component which is one (Y
is the
canonical statistic vector).
The mean value parameter is the vector \mu = E(Y)
having
components
\mu_i = E(Y_i) = \Pr(Y_i = 1).
The mean value parameter vector \mu
is given as a function
of the canonical parameter vector \theta
by
\mu_i = \frac{e^{\theta_i}}{\sum_{j = 1}^d e^{\theta_j}},
where d
is the dimension of Y
and \theta
and \mu
. This transformation is not one-to-one;
adding the same number
to each component of \theta
does not change the value
of \mu
.
The cumulant function is
c(\theta) = \log\left(\sum_{j = 1}^d e^{\theta_j}\right).
This distribution is degenerate. The sum of the components of the
canonical statistic is equal to one with probability
one, which implies the nonidentifiability of the d
-dimensional
canonical parameter vector mentioned above. Hence one parameter
(at least) is always constrained to to be zero in
fitting an aster model with a multinomial family.
This distribution has many degenerate distributions. For any vector
\delta
the limit of distributions having canonical
parameter vectors \theta + s \delta
as
s \to \infty
exists and is another
multinomial distribution (the limit distribution in the direction
\delta
).
Let A
be the set of i
such that
\delta_i = \max(\delta)
,
where \max(\delta)
denotes the maximum over the
components of \delta
.
Then the limit distribution in the direction \delta
has components Y_i
of the canonical statistic
for i \notin A
concentrated at zero.
The cumulant function of this degenerate distribution is
c(\theta) = \log\left(\sum_{j \in A} e^{\theta_j}\right).
The canonical parameters \theta_j
for j \notin A
are not identifiable, and one other canonical parameter is not
identifiable because of the constraint that the sum of the components
of the canonical statistic is equal to one with probability one.
For predecessor (sample size) n
, the successor is the sum of
n
IID multinomial-sample-size-one random vectors, that is,
multinomial with sample size n
. The mean value parameter is n
times the mean value parameter for sample size one; the cumulant function
is n
times the cumulant function for sample size one; the canonical
parameter is the same for all sample sizes.
a list of class "astfam"
giving name and values of any
hyperparameters.
fam.bernoulli()
fam.multinomial(4)
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