Type-1 Censoring*

9. Type-1 Censoring*#

Bartholomew (1957)#

This is our first introduction to censoring.

It is adapted from Unit 6: exponential1.odc.

Dataset and table from: A Problem in Life Testing (Bartholemew 1957).

Results of a life test on ten pieces of equipment#

Item No.	1	2	3	4	5	6	7	8	9	10
Date of installation	11 June	21 June	22 June	2 July	21 July	31 July	31 July	1 Aug.	2 Aug.	10 Aug.
Date of Failure	13 June	-	12 Aug.	-	23 Aug.	27 Aug.	14 Aug.	25 Aug.	6 Aug.	-
Life in Days	2	(119)	51	(77)	33	27	14	24	4	(37)
No. Days to End of Period	81	72	70	60	41	31	31	30	29	21

The observations above are the result of a test on the lifespan of some piece of equipment. The test had to end on August 31, but items 2, 4, and 10 did not fail by that date (the values in parentheses are the eventual lifespans of those pieces of equipment, but they were not known on August 31). So all we know for the purposes of the experiment is that they worked for 72, 60, and 21 days, respectively, and that they will continue working for some unknown additional time period.

The goal of the experiment is to provide a lifespan estimate. We could:

Take the censored data as observed:
- Divide the total observed working time (308 days) and divide by the equipment count (10) to get an average lifetime of 308 for an estimate of 30.8 days.
- Problem: underestimates actual average lifespan.
Ignore the equipment that didn’t fail:
- Take mean lifetime of the pieces of equipment that broke within the experiment period for an estimate of 22.1 days.
- Problems: selection bias, underestimates even more.
Use the classical method:
- Maximum Likelihood Estimation (MLE) under an exponential model. Total observed lifetime divided by 7 (number of observed failures) for an estimate of 44.0 days.
- Problem: small sample size.

The actual mean lifespan of all pieces of equipment was 38.8 days.

A Bayesian approach#

We will right-censor the three machines that still hadn’t failed by August 31, following this example.

# gamma dist parameters
α = 0.01
β = 0.1

# max possible observed life for each piece of equipment (before end of experiment)
censored = (81, 72, 70, 60, 41, 31, 31, 30, 29, 21)

# observed life within experiment dates
y = (2, 72, 51, 60, 33, 27, 14, 24, 4, 21)


with pm.Model() as m:
    λ = pm.Gamma("λ", α, β)
    μ = pm.Deterministic("μ", 1 / λ)
    obs_latent = pm.Exponential.dist(λ)

    observed = pm.Censored(
        "observed",
        obs_latent,
        lower=None,
        upper=censored,
        observed=y,
        shape=len(y),
    )

    trace = pm.sample(5000)

az.summary(trace, hdi_prob=0.95)

	mean	sd	hdi_2.5%	hdi_97.5%	mcse_mean	mcse_sd	ess_bulk	ess_tail	r_hat
λ	0.023	0.009	0.007	0.040	0.000	0.000	7875.0	9265.0	1.0
μ	51.195	23.267	19.535	95.164	0.278	0.197	7875.0	9265.0	1.0

BUGS results:

	mean	sd	MC_error	val2.5pc	median	val97.5pc	start	sample
lambda	0.02281	0.0086	3.66E-05	0.009229	0.02169	0.04248	1001	100000
mu	51.07	22.61	0.1018	23.54	46.09	108.4	1001	100000
observed[2]	122.8	60.45	0.2442	73.12	103.5	284.5	1001	100000
observed[4]	110.8	59.93	0.2145	61.09	91.81	269.8	1001	100000
observed[10]	72.01	60.28	0.2155	22.13	52.89	232.6	1001	100000

Okay, these results are pretty close. We end up with \(1/.023=43.5\) days, compared to \(43.8\) days for BUGS. But we’re missing the imputed values for each censored observation. We could get those using the imputed censoring method in this example, however it is limited to single censoring values rather than an array of different ones.

%load_ext watermark
%watermark -n -u -v -iv -p pytensor

Last updated: Sun Mar 09 2025

Python implementation: CPython
Python version       : 3.12.7
IPython version      : 8.29.0

pytensor: 2.26.4

arviz: 0.20.0
pymc : 5.19.1

Type-1 Censoring*

Contents

9. Type-1 Censoring*#

Bartholomew (1957)#

Results of a life test on ten pieces of equipment#

A Bayesian approach#