Showing papers by "T. W. Anderson published in 1992"

UV Exposure Reduces Immunization Rates and Promotes Tolerance to Epicutaneous Antigens in Humans: Relationship to Dose, CD1a^-DR^+ Epidermal Macrophage Induction, and Langerhans Cell Depletion

Abstract: Increasing UVB radiation at the earth's surface might have adverse effects on in vivo immunologic responses in humans. We prospectively randomized subjects to test whether epicutaneous immunization is altered by prior administration of biologically equalized doses of UV radiation. Multiple doses of antigens on upper inner arm skin (UV protected) were used to elicit contact sensitivity responses, which were quantitated by measuring increases in skin thickness. If a dose of UVB sufficient to induce redness (erythemagenic) was administered to the immunization site prior to sensitization with dinitrochlorobenzene (DNCB), we noted a marked reduction in the degree of sensitization (P less than 0.0006) that was highly dose responsive (r = 0.98). Even suberythemagenic UV (less than a visible sunburn) resulted in a decreased frequency of strongly positive responses (32%) as compared to controls (73%) (P = 0.019). The rate of immunologic tolerance to DNCB (active suppression of a subsequent repeat immunization) in the groups that were initially sensitized on skin receiving erythemagenic doses of UV was 31% (P = 0.0003). In addition, a localized moderate sunburn appeared to modulate immunization with diphenylcyclopropenone through a distant, unirradiated site (41% weak responses) as compared to the control group (9%) (P = 0.05). Monitoring antigen presenting cell content in the epidermis revealed that erythemagenic regimens induced CD1a-DR+ macrophages and depleted Langerhans cells. In conclusion, relevant and even subclinical levels of UV exposure have significant down modulatory effects on the ability of humans to generate a T-cell-mediated response to antigens introduced through irradiated skin.

Nonnormal Multivariate Distributions: Inference Based on Elliptically Contoured Distributions

Abstract: : The class of elliptically contoured distributions, which includes multivariate t-distributions and contaminated normal distributions, serves as a useful generalization of the class of normal multivariate distributions. The density, marginal and conditional densities, and moments of an elliptically contoured distribution are related in a simple fashion to those of a normal distribution. The asymptotic normal distributions of the sample mean and covariance matrix are developed and are compared with the asymptotic distributions of the maximum likelihood estimators of the parameters of an elliptically contoured distribution. The class of elliptically contoured distributions serves as a model for evaluating other robust estimators. Many test procedures for normal distributions are easily modified for the elliptically contoured distributions. Further generalizations are discussed.

Introduction to Hotelling (1931) The Generalization of Student’s Ratio

Abstract: Perhaps the most frequently used statistic in statistical inference is the Student t-statistic. If X 1, …, X N are N observations from the univariate normal distribution with mean µ and variance σ2, the Student-t statistic is \(t = \sqrt {N\bar x} /s,where{\text{ }}\bar x = \sum olimits_{a = 1}^N {X_a /N} \), where \(s = \left[ {\sum olimits_{a = 1}^N {\left( {X_a - \bar x} \right)^2 /n} } \right]^{1/2} \), the sample mean, and \(s = \left[ {\sum olimits_{a = 1}^N {\left( {X_a - \bar x} \right)^2 /n} } \right]^{1/2} \), the sample standard deviation; here n = N — 1, the number of degrees of freedom. Student (1908) argued that the density of t is $$const.\left( {1 + \frac{{t^2 }}{n}} \right)^{ - \left( {n + 1} \right)/2} \cdot $$ .

Introduction to Hotelling (1936) Relations Between Two Sets of Variates

Abstract: Let the random vector* x with mean 0 be partitioned into subvectors of s and t components, respectively, \(x = \left( {{x^{{{\left( 1 \right)}^\prime }}}} \right.,{\left. {{x^{{{\left( 2 \right)}^\prime }}}} \right)^\prime }\), and let the covariance matrix matrix ℰ \(xx' = \sum \) be partitioned conformally $$ Exx\prime = \left[ {\begin{array}{*{20}{c}} {E{x^{(1)}}{x^{(1)\prime }}\,E{x^{(2)}}{x^{(1)\prime }}} \\ {E{x^{(2)}}{x^{(1)\prime }}\,E{x^{(1)}}{x^{(1)\prime }}} \end{array}\,} \right] = \left[ {\begin{array}{*{20}{c}} {{\sum _{11}}\,{\sum _{12}}} \\ {{\sum _{21}}\,{\sum _{22}}} \end{array}} \right] = \sum . $$ (1)