Integration of hand-reared gorilla infants in a group

Though many zoo-born gorilla infants still have to be hand-reared and sooner or later associated with conspecifics, preferably a group, neither the managing procedures nor the process of social integration has been recorded in detail thus far. The present study, conducted at the Frankfurt Zoo, deals with a simultaneous introduction of two hand-reared infants, a male and a female, into a group. The procedure of the introduction was documented. Behavioral observations were made during 1.5 years of integration by using ad libitum and focal-animal sampling methods. The findings were compared with some data collected during a previous introduction conducted by a different procedure. The results show that both introductions were successful insofar as the infants were able to live within the group after a given time. But social integration by the new introduction procedure took much longer than previous attempts.     

Stepwise Variable Selection in Classification Problems

Two variable selection procedures are evaluated for classification problems: a forward stepwise discrimination procedure, and a stepwise procedure preceded by a preliminary screening of variables on the basis of individual t statistics. Expected probability of correct classification is used as the measure of performance. A comparison is made of the procedures using samples from multi-variate normal populations and from several nonnormal populations. The study demonstrated some situations where the use of all variables is preferable to the use of a stepwise discriminant procedure stopping after a few steps, though usually the latter procedure was superior in performance. However where the stepwise procedure performed better than using all variables, the modified stepwise procedure performed still better. The use of modified stepwise procedures in which not all the covariances of the problem need be estimated seems promising.     

Using Discrete Distributions to Compute Powers for Goodness-of-Fit Test Statistics in a One-Way Multinomial Setting

Both the Bionomial and Poisson distributions are employed in this study to compute approximate powers for goodness-of-fit test statistics. The procedure adopted involves simulating 1000 samples from each of these distributions. These samples are then employed to compute both the randomized nominal critical values and estimated powers. The type I error rates returned from the use of the randomized critical levels Cα fall within the acceptable regions. We illustrate the use of the procedure with the Pearson's X² test statistic and show that this can readily be extended to any of the other well known goodness-of-fit test statistics.     

To some reasonable test procedures in multiple contingency tables to investigate certain epidemiological or medicin-sociological relationships

One of the most important tasks of the application of mathematical-statistical methods consists in giving help in the search for possible relationships, and connected with this, the specification of new hypotheses. The progress of both the special diciplines of sciences and mathematical statistics itself leads to the application of more and more complex, that means multivariate, methods. In medical fields, especially in epidemiological and medicin-sociological studies, this fact means the necessity of analysing multidimensional contingency tables. The above formulated problem is equivalent to the problem of fitting an appropriate mathematical model (for contingency tables is this a log-linear model) to the data in a way which makes the structural relationships clear to us. In this paper it is shown that one is able to get to well-interpretable models of independence with relatively simple means. Two stepwise test procedures are described yielding essentially the same results: a so called reduction procedure which is particularly profitable in sparsely occupied tables and a procedure which uses a combination of hypotheses of conditional pairwise independence.     

A Note on Lancaster's Procedure for the Combination of Independent Events

Lancaster (1961) generalized Fisher's (1932) nonparametric procedure for combining independent p-values by transforming P_i from the i-th experiment to a chi-squared random variable with d_i degrees of freedom, with d_i not necessarily equal to 2. We explore the relationship between Lancaster's procedure and a weighted Lipták procedure (Koziol and Tuckwell, 1994) under which P_i is transformed to the standard normal scale. We investigate approximations to the null distribution of Lancaster's test procedure, chi-squared with d degrees of freedom. We find that the Cornish-Fisher (1960) expansions and the Lugannani-Rice (1980) saddlepoint approximations are quite accurate, for non-integral values of d, and for values of d as low as 20.