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作者：KEMPERMAN, JHB
摘要：We derive necessary and sufficient conditions in order that each mixture of a given family of probability densities have no more than s modal intervals, with special attention to ordinary unimodality and strong unimodality of such mixtures.
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作者：FAN, JQ
摘要：Deconvolution problems arise in a variety of situations in statistics. An interesting problem is to estimate the density f of a random variable X based on n i.i.d. observations from Y = X + epsilon, where epsilon is a measurement error with a known distribution. In this paper, the effect of errors in variables of nonparametric deconvolution is examined. Insights are gained by showing that the difficulty of deconvolution depends on the smoothness of error distributions: the smoother, the harder...
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作者：GOUTIS, C; CASELLA, G
作者单位：Cornell University
摘要：Confidence intervals for the variance of a normal distribution with unknown mean are constructed which improve upon the usual shortest interval based on the sample variance alone. These intervals have guaranteed coverage probability uniformly greater than a predetermined value 1 - alpha and have uniformly shorter length. Using information relating the size of the sample mean to that of the sample variance, we smoothly shift the usual minimum length interval closer to zero, simultaneously bring...
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作者：COX, DD
摘要：An asymptotic analysis is presented for estimation in the three-parameter first-order autoregressive model, where the parameters are the mean, autoregressive coefficient and variance of the shocks. The nearly nonstationary asymptotic model is considered wherein the autoregressive coefficient tends to 1 as sample size tends to infinity. Three different estimators are considered: the exact Gaussian maximum likelihood estimator, the conditional maximum likelihood or least squares estimator and so...
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作者：MING, GG; TZE, LL
作者单位：Stanford University
摘要：A general weak convergence theory is developed for time-sequential censored rank statistics in the two-sample problem of comparing time to failure between two treatment groups, such as in the case of a clinical trial in which patients enter serially and, after being randomly allocated to one of two treatments, are followed until they fail or withdraw from the study or until the study is terminated. Applications of the theory to time-sequential tests based on these censored rank statistics are ...
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作者：ROSENBAUM, PR
摘要：Statistics or functions are discussed that measure agreement between certain types of partially ordered data. These poset statistics are a generalization of two familiar classes of functions: the arrangement increasing functions and the decreasing reflection functions; those functions measure agreement between linearly ordered data. Specifically, the statistics in question are functions h(X1, X2) of two matrix arguments, each having N rows and they measure the agreement of the ordering of the ...
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作者：BARRON, AR
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作者：HEITJAN, DF; RUBIN, DB
作者单位：Harvard University
摘要：We present a general statistical model for data coarsening, which includes as special cases rounded, heaped, censored, partially categorized and missing data. Formally, with coarse data, observations are made not in the sample space of the random variable of interest, but rather in its power set. Grouping is a special case in which the degree of coarsening is known and nonstochastic. We establish simple conditions under which the possible stochastic nature of the coarsening mechanism can be ig...
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作者：STONE, CJ
摘要：Consider a Y-valued response variable having a density function f(.\x) that depends on an X-valued input variable x. It is assumed that X and Y are compact intervals and that f(.\.) is continuous and positive on X x Y. Let F(.\x) denote the distribution function of f(.\x) and let Q(.\x) denote its quantile function. A finite-parameter exponential family model based on tensor-product B-splines is constructed. Maximum likelihood estimation of the parameters of the model based on independent obse...
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作者：COX, DD; LLATAS, I
作者单位：Simon Bolivar University
摘要：The nearly nonstationary first-order autoregression is a sequence of autoregressive processes y(n)(k + 1) = phi-n y(n)(k) + epsilon(k + 1), 0 less-than-or-equal-to k less-than-or-equal-to n, where the epsilon(k)'s are iid mean zero shocks and the autoregressive coefficient phi-n = 1 - beta/n for some beta > 0, so that phi-n --> 1 as n --> infinity. We consider a class of maximum likelihood type estimators called M estimators, which are not necessarily robust. The estimates are obtained as the ...
