Statistical inference on mean dioptric power: asymmetric powers and singular covariance

Methods have been developed recently for testing hypotheses on mean dioptric power and for constructing confidence regions in situations that are most likely to be encountered. In this paper the methods are extended to make the analysis complete. A new situation covered specifically is that of dioptric power not of the form sphere/cylinder × axis. Such powers, termed asymmetric powers because the dioptric power matrices are asymmetric, include the equivalent power of a thick obliquely crossed bitoric lens. A second situation is that in which the covariance matrix of the sample of powers is singular. Symmetric dioptric power (the more familiar form of power) can be represented by a point in three‐dimensional space. In general, however, dioptric power is four dimensional in character. Singularity of covariance arises when variation in (he sample is limited to a subspace of dimension less than the full three or four. The space spanned by the sample is called the range space of the sample. The dimension of the range space may be four, three, two, one or zero. Each case is considered in turn. Numerical examples of hypothesis testing arc presented m range spaces of dimension four to one. The lest statistic devised for each case also gives the equation of the confidence region about the mean of a sample of dioptric powers. Singularity can sometimes he avoided merely by taking larger samples and by taking more accurate readings. The problem of near singularity is briefly discussed. The paper allows basic hypothesis testing on mean dioptric power and the construction of confidence regions in all possible circumstances.