Record Values Based Inaccuracy Measures with an Application to Testing Symmetry
This paper studies information-theoretic inaccuracy measures for record values and their application to testing symmetry. We study the Kerridge inaccuracy measure, cumulative residual inaccuracy, cumulative past inaccuracy, and extropy-based inaccuracy measures to the distributions of nth upper and lower k-record values. Explicit expressions are derived for several common lifetime distributions (exponential, Pareto, Weibull, and uniform), and the monotonic behaviour of these measures with respect to the record order n, the parameter k, and the distributional parameters is analysed. Building on the known characterisation that equality of upper and lower record-based inaccuracy implies symmetry, we develop a nonparametric goodness-of-fit test for symmetry. The test statistic is the difference between the estimated Kerridge inaccuracy measures for upper and lower records, and its null distribution is obtained by a bootstrap procedure that enforces symmetry. A simulation study demonstrates that the test maintains its nominal level for a variety of symmetric distributions and achieves high power against skewed alternatives, while a real-data illustration on annual maximum temperatures confirms its practical usefulness.