Showing papers on "Resampling published in 1970"
TL;DR: In this paper, the authors discussed the possible error in applying the two-sample t test to situations in which the assumptions of independence, normality, and variance equality were violated, and suggested the use of three normal scores tests and compared their use with that of the nonparametric Mann-Whitney U test.
Abstract: A recent article (Penfield & McSweeney, 1968) discussed the possible error in applying the two-sample t test to situations in which the assumptions of independence, normality, and variance equality were violated. They suggested the use of three normal scores tests and compared their use with that of the nonparametric Mann-Whitney U test. There is, however, an alternative procedure which can be quite useful in generating exact tests of hypotheses under randomization models. This involves the use of permutation tests (Fisher, 1925) which are very simple to define and can be effectively applied in small sample situations. In fact, many researchers are interested in permutation tests because the fixedeffects P test is often a good approximation to the permutation test. In addition, this latter statistic is not based upon the restrictive normal theory model and does provide an exact test of the hypothesis of equality of means. Basically permutation tests (also known as randomization tests) involve considering all possible arrangements of the data from an experiment. For example, consider the data from Exp. 2 of the Penfield and McSweeney article. They were studying the effect of previous experience with analogy items on a student's Miller Analogies Test score. The scores they reported for the PT group (previous training with analogies) and the NT group (no training) are given below. PT: 73, 59, 46, 68, 62 NT: 43, 38, 56
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TL;DR: A machine learning approach is adopted to devise variational Bayesian inference for variationalBayesian inference of random process generated by the autoregressive moving average (ARMA) linear model from non-linearity noise observations.
Abstract: Estimating hidden processes from non-linear noisy observations is particularly difficult when the parameters of these processes are not known. This paper adopts a machine learning approach to devise variational Bayesian inference for such scenarios. In particular, a random process generated by the autoregressive moving average (ARMA) linear model is inferred from non-linearity noise observations. The posterior distribution of hidden states are approximated by a set of weighted particles generated by the sequential Monte carlo (SMC) algorithm involving sampling with importance sampling resampling (SISR). Numerical efficiency and estimation accuracy of the proposed inference method are evaluated by computer simulations. Furthermore, the proposed inference method is demonstrated on a practical problem of estimating the missing values in the gene expression time series assuming vector autoregressive (VAR) data model.
TL;DR: The Statistical Test of a Partial Regression Coefficient under Zero Population Correlation (SRC) under zero population correlation was proposed by as discussed by the authors, who considered the case of zero correlation.
Abstract: (1970). Statistical Test of a Partial Regression Coefficient under Zero Population Correlation. The American Statistician: Vol. 24, No. 2, pp. 30-31.