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1.
To model an hypothesis of double monotone dependence between two ordinal categorical variables A and B usually a set of symmetric odds ratios defined on the joint probability function is subject to linear inequality constraints. Conversely in this paper two sets of asymmetric odds ratios defined, respectively, on the conditional distributions of A given B and on the conditional distributions of B given A are subject to linear inequality constraints. If the joint probabilities are parameterized by a saturated log-linear model, these constraints are nonlinear inequality constraints on the log-linear parameters. The problem here considered is a non-standard one both for the presence of nonlinear inequality constraints and for the fact that the number of these constraints is greater than the number of the parameters of the saturated log-linear model.This work has been supported by the COFIN 2002 project, references 2002133957_002, 2002133957_004. Preliminary findings have been presented at SIS (Società Italiana di Statistica) Annual Meeting, Bari, 2004. 相似文献
2.
It is known that the normal approximation is applicable for sums of non negative random variables, W, with the commonly employed couplings. In this work, we use the Stein’s method to obtain a general theorem of non uniform exponential bound on normal approximation base on monotone size bias couplings of W. Applications of the main result to give the bound on normal approximation for binomial random variable, the number of bulbs on at the terminal time in the lightbulb process, and the number of m runs are also provided. 相似文献
3.
In this work a new type of logits and odds ratios, which includes as special cases the continuation and the reverse-continuation
logits and odds ratios, are defined. We prove that these logits and odds ratios define a parameterization of the joint probabilities
of a two way contingency table. The problem of testing equality and inequality constraints on these logits and odds ratios
is examined with particular regard to two new hypotheses of monotone dependence.
Work partially supported by a MIUR2004 grant. Preliminary findings have been presented at SIS (Società Italiana di Statistica)
Annual Meeting, Torino, 2006. 相似文献
4.
《统计学通讯:理论与方法》2013,42(11):2147-2155
ABSTRACT This article considers the problem of testing equality of parameters of two exponential distributions having common known coefficient of variation, both under unconditional and conditional setup. Unconditional tests based on BLUE'S and LRT are considered. Using the Conditionality Principle of Fisher, an UMP conditional test for one-sided alternative is derived by conditioning on an ancillary. This test is seen to be uniformly more powerful than unconditional tests in certain given ranges of ancillary. Simulation studies on the power functions of the tests are done for this purpose. 相似文献
5.
Nonparametric curve estimation is an extremely common statistical procedure. While its primary purpose has been exploratory, some advances in inference have been made. This paper provides a critical review of inferential tests that make fundamental use of a key element of nonparametric smoothing, the bandwidth, to determine the significance of certain features. A major focus is on two important problems that have been tackled using bandwidth-based inference: testing for the multimodality of a density and testing for the monotonicity of a regression curve. Early research in bandwidth-based inference is surveyed, as well as recent theoretical advances. Possible future directions in bandwidth-based inference are discussed. 相似文献
6.
John Haslett Andrew Parnell 《Journal of the Royal Statistical Society. Series C, Applied statistics》2008,57(4):399-418
Summary. We propose a new and simple continuous Markov monotone stochastic process and use it to make inference on a partially observed monotone stochastic process. The process is piecewise linear, based on additive independent gamma increments arriving in a Poisson fashion. An independent increments variation allows very simple conditional simulation of sample paths given known values of the process. We take advantage of a reparameterization involving the Tweedie distribution to provide efficient computation. The motivating problem is the establishment of a chronology for samples taken from lake sediment cores, i.e. the attribution of a set of dates to samples of the core given their depths, knowing that the age–depth relationship is monotone. The chronological information arises from radiocarbon (14 C) dating at a subset of depths. We use the process to model the stochastically varying rate of sedimentation. 相似文献
7.
Shuenn-Ren Cheng 《统计学通讯:理论与方法》2013,42(10):1553-1560
We observe X 1,…,X k , where X i has density f(x,θ i ) possessing monotone likelihood ratio. The best population corresponds to the largest θ i . We select the population corresponding to the largest X i . The goal is to attach the best possible p-value to the inference: the selected population has the uniquely largest θ i . Gutmann and Maymin (1987) considered the location parameter case and derived the supremum of the error probability by conditioning on S, the index of the largest X i . Using this conditioning approach, Kannan and Panchapakesan (2009) considered the problem for the gamma family. We consider here a unified approach to both the location and scale parameter cases, and obtain the supremum of the error probability without using conditioning. 相似文献
8.
Haim Shore 《统计学通讯:理论与方法》2013,42(9):1819-1841
A statistical distribution of a random variable is uniquely represented by its normal-based quantile function. For a symmetrical distribution it is S-shaped (for negative kurtosis) and inverted S-shaped (otherwise). As skewness departs from zero, the quantile function gradually transforms into a monotone convex function (positive skewness) or concave function (otherwise). Recently, a new general modeling platform has been introduced, response modeling methodology, which delivers good representation to monotone convex relationships due to its unique “continuous monotone convexity” property. In this article, this property is exploited to model the normal-based quantile function, and explored using a set of 27 distributions. 相似文献
9.
AbstractWe suggest shrinkage based technique for estimating covariance matrix in the high-dimensional normal model with missing data. Our approach is based on the monotone missing scheme assumption, meaning that missing values patterns occur completely at random. Our asymptotic framework allows the dimensionality p grow to infinity together with the sample size, N, and extends the methodology of Ledoit and Wolf (2004) to the case of two-step monotone missing data. Two new shrinkage-type estimators are derived and their dominance properties over the Ledoit and Wolf (2004) estimator are shown under the expected quadratic loss. We perform a simulation study and conclude that the proposed estimators are successful for a range of missing data scenarios. 相似文献
10.
Manas Ranjan Tripathy 《统计学通讯:模拟与计算》2015,44(10):2731-2741
Based on Stein’s famous shrinkage estimation of a multivariate normal distribution, we propose a new type of estimators of the distribution function of a random variable in a nonparametric setup. The proposed estimators are then compared with the empirical distribution function, which is the best equivariant estimator under a well-known loss function. Our extensive simulation study shows that our proposed estimators can perform better for moderate to large sample sizes. 相似文献