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861.
Markov random fields (MRFs) express spatial dependence through conditional distributions, although their stochastic behavior is defined by their joint distribution. These joint distributions are typically difficult to obtain in closed form, the problem being a normalizing constant that is a function of unknown parameters. The Gaussian MRF (or conditional autoregressive model) is one case where the normalizing constant is available in closed form; however, when sample sizes are moderate to large (thousands to tens of thousands), and beyond, its computation can be problematic. Because the conditional autoregressive (CAR) model is often used for spatial-data modeling, we develop likelihood-inference methodology for this model in situations where the sample size is too large for its normalizing constant to be computed directly. In particular, we use simulation methodology to obtain maximum likelihood estimators of mean, variance, and spatial-depencence parameters (including their asymptotic variances and covariances) of CAR models. 相似文献
862.
Larry W. Taylor 《Journal of applied statistics》2007,34(4):423-441
A militarized interstate dispute (MID) involves military conflict between states with diplomatic ties and exists because two or more states have failed to resolve their differences through diplomatic channels. Jones et al. (1996) characterize an MID as the threat, display or use of military force short of war. They analyze over 2000 disputes spanning two centuries across the globe and conclude that disputes tend to be persistent once established. In this paper, I find that the passage of time can be a favorable factor in dispute resolution, and thus historical mechanisms for dispute resolution favor ending, not extending, militarized disputes. I emphasize the use of non-parametric procedures first to estimate the hazard function and then to estimate the benefits of negotiated settlements. 相似文献
863.
文章研究了半参数变系数EV模型在线性约束条件下的估计和检验问题,当响应变量缺失、非参数部分协变量带有测量误差时,利用局部纠偏的Profile最小二乘估计、Lagrange乘子方法和借补技术构造了回归模型参数分量两类纠偏约束估计量。此外,为了检验线性约束条件,构造了借补的Profile Lagrange乘子检验统计量,并通过蒙特卡洛数值模拟验证估计量和检验统计量的有效性。 相似文献
864.
865.
Francisco Cribari-Neto Maria da Glória A. Lima 《Journal of statistical planning and inference》2011,141(11):3617-3627
The linear regression model is commonly used by practitioners to model the relationship between the variable of interest and a set of explanatory variables. The assumption that all error variances are the same (homoskedasticity) is oftentimes violated. Consistent regression standard errors can be computed using the heteroskedasticity-consistent covariance matrix estimator proposed by White (1980). Such standard errors, however, typically display nonnegligible systematic errors in finite samples, especially under leveraged data. Cribari-Neto et al. (2000) improved upon the White estimator by defining a sequence of bias-adjusted estimators with increasing accuracy. In this paper, we improve upon their main result by defining an alternative sequence of adjusted estimators whose biases vanish at a much faster rate. Hypothesis testing inference is also addressed. An empirical illustration is presented. 相似文献
866.
V. Paulauskas 《Journal of Statistical Computation and Simulation》2017,87(7):1342-1362
In the paper we compare several parameterized estimators for the positive extreme value index, which is a very important parameter appearing in the estimation of the probability of rare events. Firstly, asymptotic comparison at optimal levels of the corresponding tail index estimators is performed. Secondly, the practical validation of asymptotic results for moderate finite samples is done by means of Monte-Carlo simulations. We demonstrate that theoretical domination of the positive extreme value index estimators, which are asymptotically normal with a null asymptotic bias, is not reflected in Monte-Carlo simulations. Moreover, the estimators of such type do not demonstrate stability in the sense of empirical mean-squared error. 相似文献
867.
Walter R. Schumm Michael Higgins Lorenza Lockett Shuyi Huang Nadyah Abdullah Abdullah Asiri 《Marriage & Family Review》2017,53(1):1-23
Occasionally, scientific reports have omitted information on standard deviations, making estimates of effect sizes very difficult to impossible. In such situations, several scholars have recommended obtaining an estimate of the standard deviation of distributions by dividing the range of the distribution (highest value minus lowest value) by four. However, there appears to be little evidence to confirm the validity of this approach. Articles from 2012 to 2015 in the journal Marriage & Family Review were surveyed to find instances where demographic variables (age, education, duration of relationship, number of children) were reported with both standard deviations and ranges. Ratios between range and standard deviations were calculated by several rules of thumb or more complex formulas and compared with the actual ratios obtained. Results indicated that dividing by five in general provided a more accurate estimate of actual standard deviations but accuracy in predicting the true ratio between range and standard deviation was substantially related to the position of the mean score within the range of scores with larger divisors needed as the mean approached either the minimum or the maximum values of the demographic variable (skew). Other recent formulae for estimating the standard deviation were also evaluated, but the skew-based approach appeared to be more accurate than the others. However, further investigation in other samples is needed because the skew-based approach was derived from observation of the data here, which might not replicate in different sets of data. 相似文献
868.
A local orthogonal polynomial expansion (LOrPE) of the empirical density function is proposed as a novel method to estimate the underlying density. The estimate is constructed by matching localised expectation values of orthogonal polynomials to the values observed in the sample. LOrPE is related to several existing methods, and generalises straightforwardly to multivariate settings. By manner of construction, it is similar to local likelihood density estimation (LLDE). In the limit of small bandwidths, LOrPE functions as kernel density estimation (KDE) with high-order (effective) kernels inherently free of boundary bias, a natural consequence of kernel reshaping to accommodate endpoints. Consistency and faster asymptotic convergence rates follow. In the limit of large bandwidths LOrPE is equivalent to orthogonal series density estimation (OSDE) with Legendre polynomials, thereby inheriting its consistency. We compare the performance of LOrPE to KDE, LLDE, and OSDE, in a number of simulation studies. In terms of mean integrated squared error, the results suggest that with a proper balance of the two tuning parameters, bandwidth and degree, LOrPE generally outperforms these competitors when estimating densities with sharply truncated supports. 相似文献
869.
The growth rate of the gross domestic product (GDP) usually carries heteroscedasticity, asymmetry and fat-tails. In this study three important and significantly heteroscedastic GDP series are examined. A Normal, normal-mixture, normal-asymmetric Laplace distribution and a Student's t-Asymmetric Laplace (TAL) distribution mixture are considered for distributional fit comparison of GDP growth series after removing heteroscedasticity. The parameters of the distributions have been estimated using maximum likelihood method. Based on the results of different accuracy measures, goodness-of-fit tests and plots, we find out that in the case of asymmetric, heteroscedastic and highly leptokurtic data the TAL-distribution fits better than the alternatives. In the case of asymmetric, heteroscedastic but less leptokurtic data the NM fit is superior. Furthermore, a simulation study has been carried out to obtain standard errors for the estimated parameters. The results of this study might be used in e.g. density forecasting of GDP growth series or to compare different economies. 相似文献
870.