季节异方差序列的季节调整问题研究

季节调整是从经济序列中剔除季节成分的重要方法。季节异方差的存在,使经典的季节调整方法无法彻底分离出季节成分,致使季节调整失败。本文针对季节异方差问题提出用于季节调整的改进的HS模型,并定义改进的HS模型构造季节异方差检验LR统计量,通过蒙特卡洛模拟方法分析该检验的检验尺度和检验功效。最后,利用我国税收总额月度序列给出实证分析,并通过对比考察了改进的HS模型方法季节调整的有效性。     

The choice of time interval in seasonal adjustment: A heuristic approach

A typical problem of the seasonal adjustment procedures arises when the series to be adjusted is subject to structural breaks. In fact, using the full span of the series can result in a biased estimation of the “true” seasonally adjusted series, with unclear evidence showed by the usual diagnostic tests. In these cases the researcher has to decide where to cut-off the observed series to obtain a homogeneous span; this is generally performed by a simple visual inspection of the graph of the series and/or using a-priori information about the occurrence of the break. In this paper we propose a statistical criterion based on a distance measure between filters, evaluating its performance with Monte Carlo experiments. The first results of this work have been presented at the XL scientific meeting of the Italian statistical society, Florence, 26–28 April 2000, benefiting of the discussion arisen there; a preliminary version of this paper circulated as ISAE working paper No. 21/2001 with the title “The Choice of Time Interval in Seasonal Adjustment: Characterization and Tools”. We thank an anonymous referee for precious suggestions. The authors are solely responsible of any remaining error.     

A comparison between several adjustment models to simulated teletraffic data

In this paper we assess the suitability of the fractional Brownian motion and the Markovian modulated Poisson process to represent modern teletraffic data when we use them in dimensioning and quality of service studies. The procedure consists in adjusting a model of each family to a stream of data simulated with a physical model that emulates the real flow of data. Then, both models are profusely studied in order to establish which properties of the original data are captured.     

A scale-location adjustment for proportional hazards deviance residuals

We show that deviance residuals derived using the proportional hazards assumption (including Cox regression) are not asymptotically standard normal, but that a scale-location adjustment makes them nearly standard normal, even for moderate sample sizes. This adjustment should aid in outlier detection, as it allows a more exact assessment of when a deviance residual is unusually large.     

A a comparison of the markov chain and the integral equation approaches for evaluating the run length distribution of quality control charts

Two methods that are often used to evaluate the run length distribution of quality control charts are the Markov chain and integral equation approaches. Both methods have been used to evaluate the cumulative sum (CUSUM) charts and the exponentially weighted moving average (EWMA) control charts. The Markov chain approach involves "discretiz-ing" the possible values which can be plotted. Using properties of finite Markov chains, expressions for the distribution of the run length, and for the average run length (ARL), can be obtained. For the CUSUM and EWMA charts there exist integral equations whose solution gives the ARL. Approximate methods can then be used to solve the integral equation. In this article we show that if the product midpoint rule is used to approximate the integral in the integral equation, then both approaches yield the same approximations for the ARL. In addition we show that the recursive expressions for the probability functions are the same for the two approaches. These results establish the integral equation approach as preferable whenever an integral equation can be found     

A seasonal analysis of Chinese births

Investigation of monthly Chinese births in Singapore, Malaysia, Hong Kong and Taiwan shows a very similar seasonal pattern. The strong influence of Chinese culture appears to be the cause of the seasonality and similarity. Economic development has not altered this seasonal pattern significantly. The statistical methods presented in this paper to analyze Chinese births are readily applicable to many other areas.     

A Dirichlet random coefficient regression model for quality indicators

We present a random coefficient regression model in which a response is linearly related to some explanatory variables with random coefficients following a Dirichlet distribution. These coefficients can be interpreted as weights because they are nonnegative and add up to one. The proposed estimation procedure combines iteratively reweighted least squares and the maximization on an approximated likelihood function. We also present a diagnostic tool based on a residual Q–Q plot and two procedures for estimating individual weights. The model is used to construct an index for measuring the quality of the railroad system in Spain.     

Spectral properties of the concurrent and forecasting seasonal linear filters of the X-11-ARIMA method

This study analyzes the properties of the linear filters of the X-11-ARIMA seasonal adjustment method applied for current seasonal adjustment. It provides the general formula for the combined weights that result from the ARIMA model extrapolation filters with the X-11 seasonal-adjustment filters. The three cases studied correspond to the three ARIMA models automatically tested by the X-11-ARIMA program, namely, (0, 1, 1)(0, 1, 1), (0, 2, 2)(0, 1, 1), and (2, 1. 2)(0, 1,1). The parameter values chosen reflect different degrees of flexibility of the trend-cycle and seasonal components. It is shown that the X-11-ARIMA linear filters for current seasonal adjustment are very flexible; they change with both the ARIMA extrapolation model and its parameter values, contrary to those of the X-11 program, which are fixed for a given set of options.     

A statistical testing framework for evaluating the quality of measurement processes

In this paper we address the evaluation of measurement process quality. We mainly focus on the evaluation procedure, as far as it is based on the numerical outcomes for the measurement of a single physical quantity. We challenge the approach where the ‘exact’ value of the observed quantity is compared with the error interval obtained from the measurements under test and we propose a procedure where reference measurements are used as ‘gold standard’. To this purpose, we designed a specific t-test procedure, explained here. We also describe and discuss a numerical simulation experiment demonstrating the behaviour of our procedure.     

A general caxedorical data memddology for evaluating medical diagnostic tests

Measures of sensitivity, predictive accuracy, and agreement are currently used to evaluate the efficiency of diagnostic tests reported on dichotomous scales. This paper presents a unified approach to the evaluation of diagnostic tests in terns of generalized Lnuices of sensitivity, misclassification, predictive accuracy and inaccuracy, classification agreement and prediction agreement for polytomous measurement scales. It Is sufficiently general to accommodate additional complications of study design factors such as multiple testing, known and unknown disease prevalence distributions, and multiple subpopulations defined by the cross-classification of independent factors. Estimation and hypothesis testing are developed within a general linear models approach to the analysis of categorical data from repeated measurement lesigns using weighted least squares computations. This methodology is illustrated within the context of data from a. large cotmiunity-based epidemiologic study of obstructive airways disease,Two diagnostic criteria for inpaired lung function are compared on the basis of their generalized sensitivity and classification agreement measures. The outcomes of the tests are reported on the same three-point scale (normal, questionable, impaired) and are examined within several subpopulations determined by age and sex.     

A note on a family of criteria for evaluating test statistics

ABSTRACT

In noting that the usual criteria for choosing an optimal test, Uniform Power and Local Power are at opposite ends of a spectrum of dominance criteria, a complete “Power Dominance” family of criteria for classifying and choosing optimal tests on the basis of their power characteristics is identified, wherein successive orders of dominance attach increasing weight to power close to the null hypothesis. Indices of the extent to which a preferred test has superior power characteristics over other members in its class, and an index of the proximity of a test to the envelope function of alternative tests are also provided. The ideas are exemplified using various optimal test statistics for Normal and Laplace population distributions.     

A risk set adjustment for proportional hazards modeling of combined cohort data

Sporting careers observed over a preset time interval can be partitioned into two distinct subsamples. These samples consist of individuals whose careers had already commenced at the start of the time interval (prevalent subsample) and individuals whose careers began during the time interval (incident subsample) as well the respective individual-level covariate data such as salary, height, weight, performance statistics, draft position, etc. Under the assumption of a proportional hazards model, we propose a partial likelihood estimator to model the effect of covariates on survival via an adjusted risk set sampling procedure for when the incident cohort data is used in conjunction with the prevalent cohort data. We use simulated failure time data to validate the combined cohort proportional hazards methodology and illustrate our model using an NBA data set for career durations measured between 1990 and 2008.     

A Bayesian adaptive blinded sample size adjustment method for risk differences

Adaptive sample size adjustment (SSA) for clinical trials consists of examining early subsets of on trial data to adjust estimates of sample size requirements. Blinded SSA is often preferred over unblinded SSA because it obviates many logistical complications of the latter and generally introduces less bias. On the other hand, current blinded SSA methods for binary data offer little to no new information about the treatment effect, ignore uncertainties associated with the population treatment proportions, and/or depend on enhanced randomization schemes that risk partial unblinding. I propose an innovative blinded SSA method for use when the primary analysis is a non‐inferiority or superiority test regarding a risk difference. The method incorporates evidence about the treatment effect via the likelihood function of a mixture distribution. I compare the new method with an established one and with the fixed sample size study design, in terms of maximization of an expected utility function. The new method maximizes the expected utility better than do the comparators, under a range of assumptions. I illustrate the use of the proposed method with an example that incorporates a Bayesian hierarchical model. Lastly, I suggest topics for future study regarding the proposed methods. Copyright © 2015 John Wiley & Sons, Ltd.     

A note on using the F-measure for evaluating record linkage algorithms

Record linkage is the process of identifying and linking records about the same entities from one or more databases. Record linkage can be viewed as a classification problem where the aim is to decide whether a pair of records is a match (i.e. two records refer to the same real-world entity) or a non-match (two records refer to two different entities). Various classification techniques—including supervised, unsupervised, semi-supervised and active learning based—have been employed for record linkage. If ground truth data in the form of known true matches and non-matches are available, the quality of classified links can be evaluated. Due to the generally high class imbalance in record linkage problems, standard accuracy or misclassification rate are not meaningful for assessing the quality of a set of linked records. Instead, precision and recall, as commonly used in information retrieval and machine learning, are used. These are often combined into the popular F-measure, which is the harmonic mean of precision and recall. We show that the F-measure can also be expressed as a weighted sum of precision and recall, with weights which depend on the linkage method being used. This reformulation reveals that the F-measure has a major conceptual weakness: the relative importance assigned to precision and recall should be an aspect of the problem and the researcher or user, but not of the particular linkage method being used. We suggest alternative measures which do not suffer from this fundamental flaw.     

我国宏观经济指标周期波动相关性的互谱分析

时间序列谱分析方法可以从频域角度反映序列周期波动特征的全部信息 ,它的基本思想是把时间序列看作互不相关的不同频率分量的叠加 ,利用富氏变换等手段将各频率分量加以分解 ,通过谱密度函数来衡量各分量的相对重要性以找出序列中存在的主要频率分量。我们曾利用该方法对我国转轨时期主要经济指标的周期波动特征进行了分析 ,结果表明 :80年代以来 ,我国经济增长中出现了 7～ 9年为主的中周期波动 ,这与西方工业国家曾出现的主周期波动即朱格拉周期是基本一致的 ;此外 ,围绕 2～ 3年还存在一个作用相对较弱的短周期波动。谱分析方法不但能分…     

A block bootstrap comparison for sparse chains

In this paper we apply the sequential bootstrap method proposed by Collet et al. [Bootstrap Central Limit theorem for chains of infinite order via Markov approximations, Markov Processes and Related Fields 11(3) (2005), pp. 443–464] to estimate the variance of the empirical mean of a special class of chains of infinite order called sparse chains. For this process, we show that we are able to compute numerically the true value of the standard error with any fixed error.

Our main goal is to present a comparison, for sparse chains, among sequential bootstrap, the block bootstrap method proposed by Künsch [The jackknife and the Bootstrap for general stationary observations, Ann. Statist. 17 (1989), pp. 1217–1241] and improved by Liu and Singh [Moving blocks jackknife and Bootstrap capture week dependence, in Exploring the limits of the Bootstrap, R. Lepage and L. Billard, eds., Wiley, New York, 1992, pp. 225–248] and the bootstrap method proposed by Bühlmann [Blockwise bootstrapped empirical process for stationary sequences, Ann. Statist. 22 (1994), pp. 995–1012].     

A continuous approximation for evaluating reliability of complex systems under stress-strength model.

Evaluation of system reliability for complex systems based on Taylor's approximation becomes increasingly intractable. Taguchi's concept of random experimentation has been exploited by English et al (1996) for discretization of complex systems and determination of reliability values. We indicate a few demerits of discretization method and propose to retain the continuous character of the original problem by evaluating system reliability using a range approximation method. Our proposed method works better than discretization approach in all the three engineering problems considered for the purpose of demonstration.     

A graphical method for evaluating mixture designs with respect to the slope

Good estimation of the slopes of the mixture response function may be important as well as estimation of mean mixture response. It is possible to evaluate and compare several mixture designs with respect to the slope. A graphical method is proposed that allows us to evaluate a given design's support for the fitted model in terms of slope variance. We can plot variances of slopes along Cox direction or axial direction according to existence of restriction of simplex region or not when comparing several different mixture designs.