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NAG Library Function Document

nag_censored_normal (g07bbc)

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

▸▿ 10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

1

Purpose

nag_censored_normal (g07bbc) computes maximum likelihood estimates and their standard errors for arguments of the Normal distribution from grouped and/or censored data.

2

Specification

#include <nag.h>

#include <nagg07.h>

void	nag_censored_normal (Nag_CEMethod method, Integer n, const double x[], const double xc[], const Integer ic[], double xmu, double xsig, double tol, Integer maxit, double sexmu, double sexsig, double corr, double dev, Integer nobs[], Integer nit, NagError fail)

3

Description

A sample of size

n

is taken from a Normal distribution with mean

μ

and variance

σ^{2}

and consists of grouped and/or censored data. Each of the

n

observations is known by a pair of values

(L_{i}, U_{i})

such that:

L_{i} \leq x_{i} \leq U_{i} .

The data is represented as particular cases of this form:

exactly specified observations occur when $L_{i} = U_{i} = x_{i}$ ,
right-censored observations, known only by a lower bound, occur when $U_{i} \to \infty$ ,
left-censored observations, known only by a upper bound, occur when $L_{i} \to - \infty$ ,
and interval-censored observations when $L_{i} < x_{i} < U_{i}$ .

Let the set

A

identify the exactly specified observations, sets

B

and

C

identify the observations censored on the right and left respectively, and set

D

identify the observations confined between two finite limits. Also let there be

r

exactly specified observations, i.e., the number in

A

. The probability density function for the standard Normal distribution is

Z (x) = \frac{1}{\sqrt{2 π}} \exp (- \frac{1}{2} x^{2}), - \infty < x < \infty

and the cumulative distribution function is

P (X) = 1 - Q (X) = \int_{- \infty}^{X} Z (x) d x .

The log-likelihood of the sample can be written as:

L (μ, σ) = - r \log σ - \frac{1}{2} \sum_{A} {\{(x_{i} - μ) / σ\}}^{2} + \sum_{B} \log (Q (l_{i})) + \sum_{C} \log (P (u_{i})) + \sum_{D} \log (p_{i})

where

p_{i} = P (u_{i}) - P (l_{i})

and

u_{i} = (U_{i} - μ) / σ, l_{i} = (L_{i} - μ) / σ

Let

S (x_{i}) = \frac{Z (x_{i})}{Q (x_{i})}, S_{1} (l_{i}, u_{i}) = \frac{Z (l_{i}) - Z (u_{i})}{p_{i}}

and

S_{2} (l_{i}, u_{i}) = \frac{u_{i} Z (u_{i}) - l_{i} Z (l_{i})}{p_{i}},

then the first derivatives of the log-likelihood can be written as:

\frac{\partial L (μ, σ)}{\partial μ} = L_{1} (μ, σ) = σ^{- 2} \sum_{A} (x_{i} - μ) + σ^{- 1} \sum_{B} S (l_{i}) - σ^{- 1} \sum_{C} S (- u_{i}) + σ^{- 1} \sum_{D} S_{1} (l_{i}, u_{i})

and

\frac{\partial L (μ, σ)}{\partial σ} = L_{2} (μ, σ) = - r σ^{- 1} + σ^{- 3} \sum_{A} {(x_{i} - μ)}^{2} + σ^{- 1} \sum_{B} l_{i} S (l_{i}) - σ^{- 1} \sum_{C} u_{i} S (- u_{i})

- σ^{- 1} \sum_{D} S_{2} (l_{i}, u_{i})

The maximum likelihood estimates,

\hat{μ}

and

\hat{σ}

, are the solution to the equations:

L_{1} (\hat{μ}, \hat{σ}) = 0

(1)

and

L_{2} (\hat{μ}, \hat{σ}) = 0

(2)

and if the second derivatives

\frac{\partial^{2} L}{\partial^{2} μ}

\frac{\partial^{2} L}{\partial μ \partial σ}

and

\frac{\partial^{2} L}{\partial^{2} σ}

are denoted by

L_{11}

L_{12}

and

L_{22}

respectively, then estimates of the standard errors of

\hat{μ}

and

\hat{σ}

are given by:

se (\hat{μ}) = \sqrt{\frac{- L_{22}}{L_{11} L_{22} - L_{12}^{2}}}, se (\hat{σ}) = \sqrt{\frac{- L_{11}}{L_{11} L_{22} - L_{12}^{2}}}

and an estimate of the correlation of

\hat{μ}

and

\hat{σ}

is given by:

\frac{L_{12}}{\sqrt{L_{12} L_{22}}} .

To obtain the maximum likelihood estimates the equations (1) and (2) can be solved using either the Newton–Raphson method or the Expectation-maximization

(E M)

algorithm of Dempster et al. (1977).

Newton–Raphson Method

This consists of using approximate estimates

\tilde{μ}

and

\tilde{σ}

to obtain improved estimates

\tilde{μ} + δ \tilde{μ}

and

\tilde{σ} + δ \tilde{σ}

by solving

\begin{array}{l} δ \tilde{μ} L_{11} + δ \tilde{σ} L_{12} + L_{1} = 0, \\ δ \tilde{μ} L_{12} + δ \tilde{σ} L_{22} + L_{2} = 0, \end{array}

for the corrections

δ \tilde{μ}

and

δ \tilde{σ}

EM Algorithm

The expectation step consists of constructing the variable

w_{i}

as follows:

if i \in A, w_{i} = x_{i}

(3)

if i \in B, w_{i} = E (x_{i} ∣ x_{i} > L_{i}) = μ + σ S (l_{i})

(4)

if i \in C, w_{i} = E (x_{i} ∣ x_{i} < U_{i}) = μ - σ S (- u_{i})

(5)

if i \in D, w_{i} = E (x_{i} ∣ L_{i} < x_{i} < U_{i}) = μ + σ S_{1} (l_{i}, u_{i})

(6)

the maximization step consists of substituting (3), (4), (5) and (6) into (1) and (2) giving:

\hat{μ} = \sum_{i = 1}^{n} {\hat{w}}_{i} / n

(7)

and

{\hat{σ}}^{2} = \sum_{i = 1}^{n} {({\hat{w}}_{i} - \hat{μ})}^{2} / \{r + \sum_{B} T ({\hat{l}}_{i}) + \sum_{C} T (- {\hat{u}}_{i}) + \sum_{D} T_{1} ({\hat{l}}_{i}, {\hat{u}}_{i})\}

(8)

where

T (x) = S (x) \{S (x) - x\}, T_{1} (l, u) = S_{1}^{2} (l, u) + S_{2} (l, u)

and where

{\hat{w}}_{i}

{\hat{l}}_{i}

and

{\hat{u}}_{i}

are

w_{i}

l_{i}

and

u_{i}

evaluated at

\hat{μ}

and

\hat{σ}

. Equations (3) to (8) are the basis of the

E M

iterative procedure for finding

\hat{μ}

and

{\hat{σ}}^{2}

. The procedure consists of alternately estimating

\hat{μ}

and

{\hat{σ}}^{2}

using (7) and (8) and estimating

\{{\hat{w}}_{i}\}

using (3) to (6).

In choosing between the two methods a general rule is that the Newton–Raphson method converges more quickly but requires good initial estimates whereas the

E M

algorithm converges slowly but is robust to the initial values. In the case of the censored Normal distribution, if only a small proportion of the observations are censored then estimates based on the exact observations should give good enough initial estimates for the Newton–Raphson method to be used. If there are a high proportion of censored observations then the

E M

algorithm should be used and if high accuracy is required the subsequent use of the Newton–Raphson method to refine the estimates obtained from the

E M

algorithm should be considered.

4

References

Dempster A P, Laird N M and Rubin D B (1977) Maximum likelihood from incomplete data via the

E M

algorithm (with discussion) J. Roy. Statist. Soc. Ser. B 39 1–38

Swan A V (1969) Algorithm AS 16. Maximum likelihood estimation from grouped and censored normal data Appl. Statist. 18 110–114

Wolynetz M S (1979) Maximum likelihood estimation from confined and censored normal data Appl. Statist. 28 185–195

5

Arguments

1: $method$ – Nag_CEMethodInput

On entry: indicates whether the Newton–Raphson or

E M

algorithm should be used.

method = Nag_CE_NR

, the Newton–Raphson algorithm is used.

method = Nag_CE_EM

, the

E M

algorithm is used.

Constraint:

method = Nag_CE_NR

Nag_CE_EM

2: $n$ – IntegerInput

On entry:

n

, the number of observations.

Constraint:

n \geq 2

3: $x [n]$ – const doubleInput

On entry: the observations

x_{i}

L_{i}

U_{i}

, for

i = 1, 2, \dots, n

If the observation is exactly specified – the exact value,

x_{i}

If the observation is right-censored – the lower value,

L_{i}

If the observation is left-censored – the upper value,

U_{i}

If the observation is interval-censored – the lower or upper value,

L_{i}

U_{i}

, (see xc).

4: $xc [n]$ – const doubleInput

On entry: if the

j

th observation, for

j = 1, 2, \dots, n

is an interval-censored observation then

xc [j - 1]

should contain the complementary value to

x [j - 1]

, that is, if

x [j - 1] < xc [j - 1]

, then

xc [j - 1]

contains upper value,

U_{i}

, and if

x [j - 1] > xc [j - 1]

, then

xc [j - 1]

contains lower value,

L_{i}

. Otherwise if the

j

th observation is exact or right- or left-censored

xc [j - 1]

need not be set.

Note: if

x [j - 1] = xc [j - 1]

then the observation is ignored.

5: $ic [n]$ – const IntegerInput

On entry:

ic [i - 1]

contains the censoring codes for the

i

th observation, for

i = 1, 2, \dots, n

ic [i - 1] = 0

, the observation is exactly specified.

ic [i - 1] = 1

, the observation is right-censored.

ic [i - 1] = 2

, the observation is left-censored.

ic [i - 1] = 3

, the observation is interval-censored.

Constraint:

ic [i - 1] = 0

1

2

3

, for

i = 1, 2, \dots, n

6: $xmu$ – double *Input/Output

On entry: if

xsig > 0.0

the initial estimate of the mean,

μ

; otherwise xmu need not be set.

On exit: the maximum likelihood estimate,

\hat{μ}

, of

μ

7: $xsig$ – double *Input/Output

On entry: specifies whether an initial estimate of

μ

and

σ

are to be supplied.

$xsig > 0.0$

xsig is the initial estimate of

σ

and xmu must contain an initial estimate of

μ

$xsig \leq 0.0$

Initial estimates of xmu and xsig are calculated internally from:

(a)	the exact observations, if the number of exactly specified observations is $\geq 2$ ; or
(b)	the interval-censored observations; if the number of interval-censored observations is $\geq 1$ ; or
(c)	they are set to $0.0$ and $1.0$ respectively.

On exit: the maximum likelihood estimate,

\hat{σ}

, of

σ

8: $tol$ – doubleInput

On entry: the relative precision required for the final estimates of

μ

and

σ

. Convergence is assumed when the absolute relative changes in the estimates of both

μ

and

σ

are less than tol.

tol = 0.0

, a relative precision of

0.000005

is used.

Constraint:

machine precision < tol \leq 1.0

tol = 0.0

9: $maxit$ – IntegerInput

On entry: the maximum number of iterations.

maxit \leq 0

, a value of

25

is used.

10: $sexmu$ – double *Output

On exit: the estimate of the standard error of

\hat{μ}

11: $sexsig$ – double *Output

On exit: the estimate of the standard error of

\hat{σ}

12: $corr$ – double *Output

On exit: the estimate of the correlation between

\hat{μ}

and

\hat{σ}

13: $dev$ – double *Output

On exit: the maximized log-likelihood,

L (\hat{μ}, \hat{σ})

14: $nobs [4]$ – IntegerOutput

On exit: the number of the different types of each observation;

nobs [0]

contains number of right-censored observations.

nobs [1]

contains number of left-censored observations.

nobs [2]

contains number of interval-censored observations.

nobs [3]

contains number of exactly specified observations.

15: $nit$ – Integer *Output

On exit: the number of iterations performed.

16: $fail$ – NagError *Input/Output

The NAG error argument (see Section 3.7 in How to Use the NAG Library and its Documentation).

6

Error Indicators and Warnings

NE_ALLOC_FAIL: Dynamic memory allocation failed.
See Section 2.3.1.2 in How to Use the NAG Library and its Documentation for further information.
NE_BAD_PARAM: On entry, argument $〈value〉$ had an illegal value.
NE_CONVERGENCE: Method has not converged in $〈value〉$ iterations.
NE_DIVERGENCE: Process has diverged.
NE_EM_PROCESS: The EM process has failed.
NE_INT: On entry, $n = 〈value〉$ .
Constraint: $n \geq 2$ .
NE_INT_ARRAY: On entry, $ic [〈value〉]$ is invalid, it contains $〈value〉$ .
NE_INTERNAL_ERROR: An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
See Section 2.7.6 in How to Use the NAG Library and its Documentation for further information.
NE_NO_LICENCE: Your licence key may have expired or may not have been installed correctly.
See Section 2.7.5 in How to Use the NAG Library and its Documentation for further information.
NE_OBSERVATIONS: On entry, effective number of observations $< 2$ .
NE_REAL: On entry, tol is invalid: $tol = 〈value〉$ .
NE_STANDARD_ERRORS: Standard errors cannot be computed.

7

Accuracy

The accuracy is controlled by the argument tol.

If high precision is requested with the

E M

algorithm then there is a possibility that, due to the slow convergence, before the correct solution has been reached the increments of

\hat{μ}

and

\hat{σ}

may be smaller than tol and the process will prematurely assume convergence.

8

Parallelism and Performance

nag_censored_normal (g07bbc) is not threaded in any implementation.

9

Further Comments

The process is deemed divergent if three successive increments of

μ

σ

increase.

10

Example

A sample of

18

observations and their censoring codes are read in and the Newton–Raphson method used to compute the estimates.

NAG Library Function Document

nag_censored_normal (g07bbc)

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

1

Purpose

2

Specification

3

Description

4

References

5

Arguments

6

Error Indicators and Warnings

7

Accuracy

8

Parallelism and Performance

9

Further Comments

10

Example

10.1

Program Text

10.2

Program Data

10.3

Program Results