共查询到20条相似文献,搜索用时 31 毫秒
1.
Pao-sheng Shen 《统计学通讯:理论与方法》2013,42(20):3319-3328
The complication in analyzing tumor data is that the tumors detected in a screening program tend to be slowly progressive tumors, which is the so-called length-biased sampling that is inherent in screening studies. Under the assumption that all subjects have the same tumor growth function, Ghosh (2008) developed estimation procedures for proportional hazards model. In this article, by modeling growth function as a function of covariates, we demonstrate that Ghosh (2008)'s approach can be extended to the case when each subject has a specific growth function. A simulation study is conducted to demonstrate the potential usefulness of the proposed estimators for the regression parameters in the proportional and additive hazards model. 相似文献
2.
Griliches and Hausman 5 and Wansbeek 11 proposed using the generalized method of moments (GMM) to obtain consistent estimators in linear regression models for longitudinal data with measurement error in one covariate, without requiring additional validation or replicate data. For usefulness of this methodology, we must extend it to the more realistic situation where more than one covariate are measured with error. Such an extension is not straightforward, since measurement errors across different covariates may be correlated. By a careful construction of the measurement error correlation structure, we are able to extend Wansbeek's GMM and show that the extended Griliches and Hausman's GMM is equivalent to the extended Wansbeek's GMM. For illustration, we apply the extended GMM to data from two medical studies, and compare it with the naive method and the method assuming only one covariate having measurement error. 相似文献
3.
Pao-Sheng Shen 《统计学通讯:模拟与计算》2013,42(10):2295-2307
Cai and Zeng (2011) proposed an additive mixed effect model to analyze clustered right-censored data. In this article, we demonstrate that the approach of Cai and Zeng (2011) can be extended to clustered doubly censored data. Furthermore, when both left- and right-censoring variables are always observed, we propose alternative estimators using the approach of Cai and Cheng (2004). A simulation study is conducted to investigate the performance of the proposed estimators. 相似文献
4.
Huang (2010) proposed an optional randomized response model using a linear combination scrambling which is a generalization of the multiplicative scrambling of Eichhorn and Hayre (1983) and the additive scrambling of Gupta et al. (2006, 2010). In this article, we discuss two main issues. (1) Can the Huang (2010) model be improved further by using a two-stage approach?; (2) Does the linear combination scrambling provide any benefit over the additive scrambling of Gupta et al. (2010)? We will note that the answer to the first question is “yes” but the answer to the second question is “no.” 相似文献
5.
Przystalski and Krajewski (2007) proposed the restricted backfitting (RBCF) estimator and restricted Speckman (RSPC) estimator for the treatment effects in a partially linear model when some additional exact linear restrictions are assumed to hold. In this article, we introduce the preliminary test backfitting (PTBCF) estimator and preliminary test Speckman (PTSPC) estimator when the validity of the restrictions is suspected. Performances of the proposed estimators are examined with respect to the mean squared error (MSE) criterion. In addition, numerical behaviors of the proposed estimators are illustrated and compared via a Monte Carlo simulation study. 相似文献
6.
Hanchao Wang 《统计学通讯:理论与方法》2013,42(3):394-407
In this article, we present the local linear estimations for diffusion coefficient and drift coefficient in the second-order diffusion model. We show that under mild conditions, the estimators are weak consistent. We also use a Monte Carlo experiment to compare our estimators with the ones in Nicolau (2007). 相似文献
7.
Housila P. Singh 《统计学通讯:理论与方法》2013,42(6):1008-1023
This paper suggests an efficient class of ratio and product estimators for estimating the population mean in stratified random sampling using auxiliary information. It is interesting to mention that, in addition to many, Koyuncu and Kadilar (2009), Kadilar and Cingi (2003, 2005), and Singh and Vishwakarma (2007) estimators are identified as members of the proposed class of estimators. The expressions of bias and mean square error (MSE) of the proposed estimators are derived under large sample approximation in general form. Asymptotically optimum estimator (AOE) in the class is identified alongwith its MSE formula. It has been shown that the proposed class of estimators is more efficient than combined regression estimator and Koyuncu and Kadilar (2009) estimator. Moreover, theoretical findings are supported through a numerical example. 相似文献
8.
We propose a class of estimators for the population mean when there are missing data in the data set. Obtaining the mean square error equations of the proposed estimators, we show the conditions where the proposed estimators are more efficient than the sample mean, ratio-type estimators, and the estimators in Singh and Horn (2000) and Singh and Deo (2003) in the case of missing data. These conditions are also supported by a numerical example. 相似文献
9.
Pao-Sheng Shen 《统计学通讯:模拟与计算》2013,42(3):603-612
In this article, we consider the M-estimators for the linear regression model when both response and covariate variables are subject to double censoring. The proposed estimators are constructed as some functional of three types of estimators for a bivariate survival distribution. The first two estimators are the generalizations of the Campbell and Földes (1982) and Dabrowska (1988) estimators proposed by Shen (2009). The third estimator is the generalization of the Prentice and Cai (1992) estimator. The consistency of the proposed M-estimators is established. A simulation study is conducted to investigate the performance of the proposed estimators. Furthermore, the simple bootstrap methods are used to estimate standard deviations and construct interval estimators. 相似文献
10.
Feng-Shou Ko 《统计学通讯:理论与方法》2013,42(15):2681-2698
A proposed method based on frailty models is used to identify longitudinal biomarkers or surrogates for a multivariate survival. This method is an extention of earlier models by Wulfsohn and Tsiatis (1997) and Song et al. (2002). In this article, similar to Henderson et al. (2002), a joint likelihood function combines the likelihood functions of the longitudinal biomarkers and the multivariate survival times. We use simulations to explore how the number of individuals, the number of time points per individual and the functional form of the random effects from the longitudianl biomarkers influence the power to detect the association of a longitudinal biomarker and the multivariate survival time. The proposed method is illustrate by using the gastric cancer data. 相似文献
11.
M. A. Alkhamisi 《统计学通讯:模拟与计算》2013,42(3):535-547
A number of procedures have been developed for finding biased estimators of regression parameters. One of these procedures is the ridge regression. In this article, a new approach to obtain the ridge parameter K is suggested and then evaluated by Monte Carlo simulations. A number of different models are investigated for different number of observations, the strength of correlation between the explanatory variables, and distribution of the error terms. The mean squared error (MSE) criterion is used to examine the performance of the proposed estimators when compared with other well-known estimators. Under certain conditions, it is shown that at least one of the proposed estimators have a smaller MSE than the ordinary least squared estimator (OLS) and Hoerl and Kennard (1970a) estimator (HK). 相似文献
12.
Fayçal Hamdi 《统计学通讯:理论与方法》2013,42(22):4182-4199
The purpose of this article is to develop algorithms for computing the exact Fisher information matrix of periodic time-varying state-space models. We first present a relatively simple recursive algorithm which computes the elements of the exact information matrix without involving numerical differentiation, since all required derivatives are analytically evaluated. The proposed algorithm extends the procedure due to Cavanaugh and Shumway (1996) to the periodic state-space framework. Exploiting the approach used in Klein et al. (2000), a second algorithm is proposed in order to obtain the exact information matrix as a whole instead of element by element. The algorithms are first developed in a general framework and then specialized to the case of a periodic Gaussian vector autoregressive moving-average (PVARMA) model. 相似文献
13.
Hu Yang 《统计学通讯:理论与方法》2013,42(1):70-80
Sakall?oglu et al. (2001) dealt with the comparisons among the ridge estimator, Liu estimator, and iteration estimator. Akdeniz and Erol (2003) have compared the (almost unbiased) generalized ridge regression estimator with the (almost unbiased) generalized Liu estimator in the matrix mean squared error sense. In this article, we study the ridge estimator and Liu estimator with respect to linear equality restriction, and establish some sufficient conditions for the superiority of the restricted ridge estimator over the restricted Liu estimator and the superiority of the restricted Liu estimator over the restricted ridge estimator under mean squared error matrix, respectively. Furthermore, we give a numerical example. 相似文献
14.
Difference-based estimators for the error variance are popular since they do not require the estimation of the mean function. Unlike most existing difference-based estimators, new estimators proposed by Müller et al. (2003) and Tong and Wang (2005) achieved the asymptotic optimal rate as residual-based estimators. In this article, we study the relative errors of these difference-based estimators which lead to better understanding of the differences between them and residual-based estimators. To compute the relative error of the covariate-matched U-statistic estimator proposed by Müller et al. (2003), we develop a modified version by using simpler weights. We further investigate its asymptotic property for both equidistant and random designs and show that our modified estimator is asymptotically efficient. 相似文献
15.
ABSTRACT This paper reviews and extends the literature on the finite sample behavior of tests for sample selection bias. Monte Carlo results show that, when the “multicollinearity problem” identified by Nawata (1993) is severe, (i) the t-test based on the Heckman–Greene variance estimator can be unreliable, (ii) the Likelihood Ratio test remains powerful, and (iii) nonnormality can be interpreted as severe sample selection bias by Maximum Likelihood methods, leading to negative Wald statistics. We also confirm previous findings (Leung and Yu, 1996) that the standard regression-based t-test (Heckman, 1979) and the asymptotically efficient Lagrange Multiplier test (Melino, 1982), are robust to nonnormality but have very little power. 相似文献
16.
In this paper, we investigate the effect of pre-smoothing on model selection. Christóbal et al 6 showed the beneficial effect of pre-smoothing on estimating the parameters in a linear regression model. Here, in a regression setting, we show that smoothing the response data prior to model selection by Akaike's information criterion can lead to an improved selection procedure. The bootstrap is used to control the magnitude of the random error structure in the smoothed data. The effect of pre-smoothing on model selection is shown in simulations. The method is illustrated in a variety of settings, including the selection of the best fractional polynomial in a generalized linear model. 相似文献
17.
Constantinos Petropoulos 《统计学通讯:理论与方法》2013,42(17):3153-3162
Under Stein's loss, a class of improved estimators for the scale parameter of a mixture of exponential distribution with unknown location is constructed. The method is analogous to Maruyama's (1998) construction for the variance of a normal distribution and also an extension of the result produced in Petropoulos and Kourouklis (2002). Also, robustness properties are considered. 相似文献
18.
Consider a skewed population. Suppose an intelligent guess could be made about an interval that contains the population mean. There may exist biased estimators with smaller mean squared error than the arithmetic mean within such an interval. This article indicates when it is advisable to shrink the arithmetic mean towards a guessed interval using root estimators. The goal is to obtain an estimator that is better near the average of natural origins. An estimator proposed. This estimator contains the Thompson (1968) ordinary shrinkage estimator, the Jenkins et al. (1973) square-root estimator, and the arithmetic sample mean as special cases. The bias and the mean squared error of the proposed more general estimator is compared with the three special cases. Shrinkage coefficients that yield minimum mean squared error estimators are obtained. The proposed estimator is considerably more efficient than the three special cases. This remains true for highly skewed populations. The merits of the proposed shrinkage square-root estimator are supported by the results of numerical and simulation studies. 相似文献
19.
This article deals with the adaptive estimation of a periodic autoregressive model, with unspecified innovation density satisfying only some general technical assumptions. We first establish, while verifying the adapted sufficient conditions of Swensen (1985) to our model, the Local Asymptotic Normality (LAN), the Local Asymptotic Quadratic (LAQ), and the Local Asymptotic properties satisfied by its central sequence. Secondly, the Locally Asymptotically Minimax (LAM) estimators are constructed. Using these results, we construct the adaptive estimators of the unknown autoregressive parameters. The performances of the established estimators are shown, via simulation studies. 相似文献
20.
Antonis Demos 《统计学通讯:理论与方法》2013,42(10):1713-1747
Extending the results in Sargan (1976) and Tanaka (1984), we derive the asymptotic expansions of the distribution, the bias and the mean squared error of the MM and QML estimators of the first-order autocorrelation and the MA parameter for the MA(1) model. It turns out that the asymptotic properties of the estimators depend on whether the mean of the process is known or estimated. A comparison of the moment expansions, either in terms of bias or MSE, reveals that there is not uniform superiority of neither of the estimators, when the mean of the process is estimated. This is also confirmed by simulations. In the zero-mean case, and on theoretical grounds, the QMLEs are superior to the MM ones in both bias and MSE terms. We also discuss how the approximations are affected by moderate deviations from the unit root case. The results presented here are important for bias correction and increasing the efficiency of the estimators. 相似文献