Application of the characterization theory to the mixture model

This paper indicates the potential application of the characterization theory to the mixture model. It discusses a particular example of a mixture model and concludes that successful application of this theory can be made in order to choose the appropriate mixture model and the mixing parameter.     

A review of image-warping methods

SUMMARY Image warping is a transformation which maps all positions in one image plane to positions in a second plane. It arises in many image analysis problems, whether in order to remove optical distortions introduced by a camera or a particular viewing perspective, to register an image with a map or template, or to align two or more images. The choice of warp is a compromise between a smooth distortion and one which achieves a good match. Smoothness can be ensured by assuming a parametric form for the warp or by constraining it using differential equations. Matching can be specified by points to be brought into alignment, by local measures of correlation between images, or by the coincidence of edges. Parametric and non-parametric approaches to warping, and matching criteria, are reviewed.     

Rejoinder to 'Ahmed, M.S. (1998). A note on regression-type estimators using multiple auxiliary information.'

In the estimators t ₃ , t ₄ , t ₅ of Mukerjee, Rao & Vijayan (1987), b _{y x} and b _{y z} are partial regression coefficients of y on x and z , respectively, based on the smaller sample. With the above interpretation of b _{y x} and b _{y z} in t ₃ , t ₄ , t ₅ , all the calculations in Mukerjee at al. (1987) are correct. In this connection, we also wish to make it explicit that b _{x z} in t ₅ is an ordinary and not a partial regression coefficient. The 'corrected' MSEs of t ₃ , t ₄ , t ₅ , as given in Ahmed (1998 Section 3) are computed assuming that our b _{y x} and b _{y z} are ordinary and not partial regression coefficients. Indeed, we had no intention of giving estimators using the corresponding ordinary regression coefficients which would lead to estimators inferior to those given by Kiregyera (1984). We accept responsibility for any notational confusion created by us and express regret to readers who have been confused by our notation. Finally, in consideration of the above, it may be noted that Tripathi & Ahmed's (1995) estimator t ₀ , quoted also in Ahmed (1998), is no better than t ₅ of Mukerjee at al. (1987).     

Distributions of the lifetimes of system components operating under an unknown common environment

SUMMARY Families of joint distributions for describing the lifetimes of a system of components that operate under an unknown environment, when the environment follows a Weibull distribution, are derived. The reliability function for this system is calculated and several properties of the aforementioned joint distributions are investigated.     

PARENTAL INFLUENCE ON GENDER AND MARITAL ROLE ATTITUDES: IMPLICATIONS FOR INTERVENTION

This study examined parental influence on specific attitudes toward marital and parental roles and nonspecific gender-role attitudes. Respondents included 173 young adults (127 women and 46 men) and their parents. Young adults' scores on the Role Orientation scale of the Marital Statisfaction Inventory and on masculinity and femininity scales from two self-report inventories (the MMPI-2 and the Personal Attributes Questionnarie) were correlated with their parents' scores on a subset of these same measures. Results indicated that (a) marital and parental role attitudes among young adults are strongly related to specific marital role attitudes of their parents but only modestly related (for women) or unrelated (for men) to nonspecific gender-role attitudes of their parents; (b) broader gender-role attitudes of young men are moderately correlated with their father's attitudes but generally uncorrelated with their mother's attitudes regarding gender roles; and (c) the relationship of young women's gender-role attitudes to both maternal and paternal attitudes is generally weaker and more complex than for young men. Clinical implications of these findings for assessment and intervention with distressed couples are discussed.     

Accommodating stochastic departures from percentile invariance in causal models

Summary.  Consider a clinical trial in which participants are randomized to a single-dose treatment or a placebo control and assume that the adherence level is accurately recorded. If the treatment is effective, then good adherers in the treatment group should do better than poor ad- herers because they received more drug; the treatment group data follow a dose–response curve. But, good adherers to the placebo often do better than poor adherers, so the observed adherence–response in the treatment group cannot be completely attributed to the treatment. Efron and Feldman proposed an adjustment to the observed adherence–response in the treatment group by using the adherence–response in the control group. It relies on a percentile invariance assumption under which each participant's adherence percentile within their assigned treatment group does not depend on the assigned group (active drug or placebo). The Efron and Feldman approach is valid under percentile invariance, but not necessarily under departures from it. We propose an analysis based on a generalization of percentile invariance that allows adherence percentiles to be stochastically permuted across treatment groups, using a broad class of stochastic permutation models. We show that approximate maximum likelihood estimates of the underlying dose–response curve perform well when the stochastic permutation process is correctly specified and are quite robust to model misspecification.