Completeness (statistics)

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In statistics, completeness is a property of a statistic in relation to a model for a set of observed data. In essence, it ensures that the distributions corresponding to different values of the parameters are distinct.

It is closely related to the idea of identifiability, but in statistical theory it is often found as a condition imposed on a sufficient statistic from which certain optimality results are derived.

Definition[]

Consider a random variable X whose probability distribution belongs to a parametric model P_θ parametrized by θ.

Say T is a statistic; that is, the composition of a measurable function with a random sample X₁,...,X_n.

The statistic T is said to be complete for the distribution of X if, for every measurable function g,:^[1]

${\displaystyle {\text{if }}\operatorname {E} _{\theta }(g(T))=0{\text{ for all }}\theta {\text{ then }}\mathbf {P} _{\theta }(g(T)=0)=1{\text{ for all }}\theta .}$

The statistic T is said to be boundedly complete for the distribution of X if this implication holds for every measurable function g that is also bounded.

Example 1: Bernoulli model[]

The Bernoulli model admits a complete statistic.^[2] Let X be a random sample of size n such that each X_i has the same Bernoulli distribution with parameter p. Let T be the number of 1s observed in the sample. T is a statistic of X which has a binomial distribution with parameters (n,p). If the parameter space for p is (0,1), then T is a complete statistic. To see this, note that

${\displaystyle \operatorname {E} _{p}(g(T))=\sum _{t=0}^{n}{g(t){n \choose t}p^{t}(1-p)^{n-t}}=(1-p)^{n}\sum _{t=0}^{n}{g(t){n \choose t}\left({\frac {p}{1-p}}\right)^{t}}.}$

Observe also that neither p nor 1 − p can be 0. Hence ${\displaystyle E_{p}(g(T))=0}$ if and only if:

${\displaystyle \sum _{t=0}^{n}g(t){n \choose t}\left({\frac {p}{1-p}}\right)^{t}=0.}$

On denoting p/(1 − p) by r, one gets:

${\displaystyle \sum _{t=0}^{n}g(t){n \choose t}r^{t}=0.}$

First, observe that the range of r is the positive reals. Also, E(g(T)) is a polynomial in r and, therefore, can only be identical to 0 if all coefficients are 0, that is, g(t) = 0 for all t.

It is important to notice that the result that all coefficients must be 0 was obtained because of the range of r. Had the parameter space been finite and with a number of elements less than or equal to n, it might be possible to solve the linear equations in g(t) obtained by substituting the values of r and get solutions different from 0. For example, if n = 1 and the parameter space is {0.5}, a single observation and a single parameter value, T is not complete. Observe that, with the definition:

${\displaystyle g(t)=2(t-0.5),\,}$

then, E(g(T)) = 0 although g(t) is not 0 for t = 0 nor for t = 1.

Relation to sufficient statistics[]

For some parametric families, a complete sufficient statistic does not exist (for example, see Galili and Meilijson 2016 ^[3]). Also, a minimal sufficient statistic need not exist. (A case in which there is no minimal sufficient statistic was shown by Bahadur in 1957.^{[citation needed]}) Under mild conditions, a minimal sufficient statistic does always exist. In particular, these conditions always hold if the random variables (associated with P_θ ) are all discrete or are all continuous.^{[citation needed]}

Importance of completeness[]

The notion of completeness has many applications in statistics, particularly in the following two theorems of mathematical statistics.

Lehmann–Scheffé theorem[]

Completeness occurs in the Lehmann–Scheffé theorem,^[4] which states that if a statistic that is unbiased, complete and sufficient for some parameter θ, then it is the best mean-unbiased estimator for θ. In other words, this statistic has a smaller expected loss for any convex loss function; in many practical applications with the squared loss-function, it has a smaller mean squared error among any estimators with the same expected value.

Examples exists that when the minimal sufficient statistic is not complete then several alternative statistics exist for unbiased estimation of θ, while some of them have lower variance than others.^[5]

See also minimum-variance unbiased estimator.

Basu's theorem[]

Bounded completeness occurs in Basu's theorem,^[6] which states that a statistic that is both boundedly complete and sufficient is independent of any ancillary statistic.

Bahadur's theorem[]

Bounded completeness also occurs in . In the case where there exists at least one minimal sufficient statistic, a statistic which is sufficient and boundedly complete, is necessarily minimal sufficient.

Notes[]

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References[]

• Basu, D. (1988). J. K. Ghosh (ed.). Statistical information and likelihood : A collection of critical essays by Dr. D. Basu. Lecture Notes in Statistics. 45. Springer. ISBN 978-0-387-96751-6. MR 0953081.
• Bickel, Peter J.; Doksum, Kjell A. (2001). Mathematical statistics, Volume 1: Basic and selected topics (Second (updated printing 2007) of the Holden-Day 1976 ed.). Pearson Prentice–Hall. ISBN 978-0-13-850363-5. MR 0443141.
• E. L., Lehmann; Romano, Joseph P. (2005). Testing statistical hypotheses. Springer Texts in Statistics (Third ed.). New York: Springer. pp. xiv+784. ISBN 978-0-387-98864-1. MR 2135927. Archived from the original on 2013-02-02.
• Lehmann, E.L.; Scheffé, H. (1950). "Completeness, similar regions, and unbiased estimation. I." Sankhyā: the Indian Journal of Statistics. 10 (4): 305–340. doi:10.1007/978-1-4614-1412-4_23. JSTOR 25048038. MR 0039201.
• Lehmann, E.L.; Scheffé, H. (1955). "Completeness, similar regions, and unbiased estimation. II". Sankhyā: The Indian Journal of Statistics. 15 (3): 219–236. doi:10.1007/978-1-4614-1412-4_24. JSTOR 25048243. MR 0072410.