Uniformly most powerful test

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In statistical hypothesis testing, a uniformly most powerful (UMP) test is a hypothesis test which has the greatest power 1 − β among all possible tests of a given size α. For example, according to the Neyman-Pearson lemma, the likelihood-ratio test is UMP for testing simple (point) hypotheses.

Setting

Let X denote a random vector (corresponding to the measurements), taken from a parametrized family of probability density functions or probability mass functions fθ(x), which depends on the unknown deterministic parameter \theta \in \Theta. The parameter space Θ is partitioned into two disjoint sets Θ0 and Θ1. Let H0 denote the hypothesis that \theta \in \Theta_0, and let H1 denote the hypothesis that \theta \in \Theta_1. The binary test of hypotheses is performed using a test function φ(x).

\phi(x) = \begin{cases} 1 & \text{if } x \in R \\ 0 & \text{if } x \in A \end{cases}

meaning that H1 is in force if the measurement X \in R and that H0 is in force if the measurement X \in A. A \cup R is a disjoint covering of the measurement space.

Formal definition

A test function φ(x) is UMP of size α if for any other test function φ'(x) we have:

\sup_{\theta\in\Theta_0}\; E_\theta\phi'(X)=\alpha'\leq\alpha=\sup_{\theta\in\Theta_0}\; E_\theta\phi(X)\,
E_\theta\phi'(X)=1-\beta'\leq 1-\beta=E_\theta\phi(X) \quad \forall \theta \in \Theta_1

The Karlin-Rubin theorem

The Karlin-Rubin theorem can be regarded as an extension of the Neyman-Pearson lemma for composite hypotheses. Consider a scalar measurement having a probability density function parameterized by a scalar parameter θ, and define the likelihood ratio l(x) = f_{\theta_1}(x) / f_{\theta_0}(x). If l(x) is monotone non-decreasing for any pair \theta_1 \geq \theta_0 (meaning that the greater x is, the more likely H1 is), then the threshold test:

\phi(x) = \begin{cases} 1 & \text{if } x > x_0 \\ 0 & \text{if } x < x_0 \end{cases}
E_{\theta_0}\phi(X)=\alpha

is the UMP test of size α for testing H_0: \theta \leq \theta_0 \text{ vs. } H_1: \theta > \theta_0

Note that exactly the same test is also UMP for testing H0:θ = θ0 vs. H1:θ > θ0

Important case: The exponential family

Although the Karlin-Rubin may seem weak because of its restriction to scalar parameter and scalar measurement, it turns out that there exist a host of problems for which the theorem holds. In particular, the one-dimensional exponential family of probability density functions or probability mass functions with fθ(x) = c(θ)h(x)exp(π(θ)T(x)) has a monotone non-decreasing likelihood ratio in the sufficient statistic T(x), provided that π(θ) is non-decreasing.

Example

Let X=(X_0 , X_1 ,\dots , X_{M-1}) denote i.i.d. normally distributed N-dimensional random vectors with mean θm and covariance matrix R. We then have

f_\theta (X) = (2 \pi)^{-M N / 2} |R|^{-M / 2} \exp \left\{-\frac{1}{2} \sum_{n=0}^{M-1}(X_n - \theta m)^T R^{-1}(X_n - \theta m) \right\} =
= (2 \pi)^{-M N / 2} |R|^{-M / 2} \exp \left\{-\frac{1}{2} \sum_{n=0}^{M-1}(\theta^2 m^T R^{-1} m) \right\} \cdot \exp \left\{-\frac{1}{2} \sum_{n=0}^{M-1}X_n^T R^{-1} X_n \right\} \cdot \exp \left\{\theta m^T R^{-1} \sum_{n=0}^{M-1}X_n \right\}

which is exactly in the form of the exponential family shown in the previous section, with the sufficient statistic being

T(X) = m^T R^{-1} \sum_{n=0}^{M-1}X_n.

Thus, we conclude that the test

\phi(T) = \begin{cases} 1 & \text{if } T > t_0 \\ 0 & \text{if } T < t_0 \end{cases}
E_{\theta_0} \phi (T) = \alpha

is the UMP test of size α for testing H_0: \theta \leq \theta_0 vs. H1:θ > θ0

Further discussion

Finally, we note that in general, UMP tests do not exist for vector parameters or for two-sided tests (a test in which one hypothesis lies on both sides of the alternative). Why is it so?

The reason is that in these situations, the most powerful test of a given size for one possible value of the parameter (e.g. for θ1 where θ1 > θ0) is different than the most powerful test of the same size for a different value of the parameter (e.g. for θ2 where θ2 < θ0). As a result, no test is Uniformly most powerful.

References

  • L. L. Scharf, Statistical Signal Processing, Addison-Wesley, 1991, section 4.7.

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Some of the initial content on this page may be incorporated in part from copyleft sources in the public domain including wikis such as Wikipedia and AskDrWiki. Drug information for patients came from the The National Library of Medicine. Infectious disease information may have come from the Centers for Disease Control (CDC). Differential Diagnoses are drawn from clinicians as well as an amalgamation of 3 sources: 1.The Disease Database; 2. Kahan, Scott, Smith, Ellen G. In A Page: Signs and Symptoms. Malden, Massachusetts: Blackwell Publishing, 2004:3; 3. Sailer, Christian, Wasner, Susanne. Differential Diagnosis Pocket. Hermosa Beach, CA: Borm Bruckmeir Publishing LLC, 2002:7 .

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