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Finite Samples (finite + sample)

Distribution by Scientific Domains
Mathematics and Statistics75%
Business, Economics, Finance and Accounting12%

Terms modified by Finite Samples

  • finite sample performance
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  • finite sample property
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    Selected Abstracts

    From Finite Sample to Asymptotic Methods in Statistics by Pranab K. Sen, Julio M. Singer, Antonio C. Pedroso de Lima

    INTERNATIONAL STATISTICAL REVIEW, Issue 2 2010
    Erkki P. Liski
    No abstract is available for this article. [source]

    Sampling bias and logistic models

    JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 4 2008
    Peter McCullagh
    Summary., In a regression model, the joint distribution for each finite sample of units is determined by a function px(y) depending only on the list of covariate values x=(x(u1),,,x(un)) on the sampled units. No random sampling of units is involved. In biological work, random sampling is frequently unavoidable, in which case the joint distribution p(y,x) depends on the sampling scheme. Regression models can be used for the study of dependence provided that the conditional distribution p(y|x) for random samples agrees with px(y) as determined by the regression model for a fixed sample having a non-random configuration x. The paper develops a model that avoids the concept of a fixed population of units, thereby forcing the sampling plan to be incorporated into the sampling distribution. For a quota sample having a predetermined covariate configuration x, the sampling distribution agrees with the standard logistic regression model with correlated components. For most natural sampling plans such as sequential or simple random sampling, the conditional distribution p(y|x) is not the same as the regression distribution unless px(y) has independent components. In this sense, most natural sampling schemes involving binary random-effects models are biased. The implications of this formulation for subject-specific and population-averaged procedures are explored. [source]

    Nonparametric Estimation and Testing in Panels of Intercorrelated Time Series

    JOURNAL OF TIME SERIES ANALYSIS, Issue 6 2004
    Vidar Hjellvik
    Abstract., We consider nonparametric estimation and testing of linearity in a panel of intercorrelated time series. We place the emphasis on the situation where there are many time series in the panel but few observations for each of the series. The intercorrelation is described by a latent process, and a conditioning argument involving this process plays an important role in deriving the asymptotic theory. To be accurate the asymptotic distribution of the test functional of linearity requires a very large number of observations, and bootstrapping gives much better finite sample results. A number of simulation experiments and an illustration on a real data set are included. [source]

    Stock Returns and Operating Performance of Securities Issuers

    THE JOURNAL OF FINANCIAL RESEARCH, Issue 3 2002
    Gil S. Bae
    Abstract We examine long-run stock returns and operating performance around firms' offerings of common stock, convertible debt, and straight debt from 1985 to 1990. We find that pre-issue abnormal returns are positive and significant for stock issuers, but not for convertible and straight debt issuers. The post-issue mean returns show that common stock and convertible debt issuers experience underperformance during the post-issue periods, but straight debt issuers do not. Consistent with these results, common stock issuers experience the best pre-issue operating performance among all three types of issuers, and operating performance declines during the post-issue periods for common stock and convertible debt issuers. Using a new approach in linear model estimations to correct heteroskedasticity and to adjust for finite sample, we find a positive relation between post-issue operating performance and issue-period stock price reactions. The results suggest that future operating performance is anticipated at the issue and that securities issues provide information on issuers' future performance. [source]

    Empirical Likelihood-Based Inference in Conditional Moment Restriction Models

    ECONOMETRICA, Issue 6 2004
    Yuichi Kitamura
    This paper proposes an asymptotically efficient method for estimating models with conditional moment restrictions. Our estimator generalizes the maximum empirical likelihood estimator (MELE) of Qin and Lawless (1994). Using a kernel smoothing method, we efficiently incorporate the information implied by the conditional moment restrictions into our empirical likelihood-based procedure. This yields a one-step estimator which avoids estimating optimal instruments. Our likelihood ratio-type statistic for parametric restrictions does not require the estimation of variance, and achieves asymptotic pivotalness implicitly. The estimation and testing procedures we propose are normalization invariant. Simulation results suggest that our new estimator works remarkably well in finite samples. [source]

    The frequency spectrum of finite samples from the intermittent silence process

    JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, Issue 4 2009
    Ramon Ferrer-i-Cancho
    It has been argued that the actual distribution of word frequencies could be reproduced or explained by generating a random sequence of letters and spaces according to the so-called intermittent silence process. The same kind of process could reproduce or explain the counts of other kinds of units from a wide range of disciplines. Taking the linguistic metaphor, we focus on the frequency spectrum, i.e., the number of words with a certain frequency, and the vocabulary size, i.e., the number of different words of text generated by an intermittent silence process. We derive and explain how to calculate accurately and efficiently the expected frequency spectrum and the expected vocabulary size as a function of the text size. [source]

    Non-parametric confidence bands in deconvolution density estimation

    JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 3 2007
    Nicolai Bissantz
    Summary., Uniform confidence bands for densities f via non-parametric kernel estimates were first constructed by Bickel and Rosenblatt. In this paper this is extended to confidence bands in the deconvolution problem g=f*, for an ordinary smooth error density ,. Under certain regularity conditions, we obtain asymptotic uniform confidence bands based on the asymptotic distribution of the maximal deviation (L, -distance) between a deconvolution kernel estimator and f. Further consistency of the simple non-parametric bootstrap is proved. For our theoretical developments the bias is simply corrected by choosing an undersmoothing bandwidth. For practical purposes we propose a new data-driven bandwidth selector that is based on heuristic arguments, which aims at minimizing the L, -distance between and f. Although not constructed explicitly to undersmooth the estimator, a simulation study reveals that the bandwidth selector suggested performs well in finite samples, in terms of both area and coverage probability of the resulting confidence bands. Finally the methodology is applied to measurements of the metallicity of local F and G dwarf stars. Our results confirm the ,G dwarf problem', i.e. the lack of metal poor G dwarfs relative to predictions from ,closed box models' of stellar formation. [source]

    Generalized least squares with misspecified serial correlation structures

    JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 3 2001
    Sergio G. Koreisha
    Summary. The regression literature contains hundreds of studies on serially correlated disturbances. Most of these studies assume that the structure of the error covariance matrix , is known or can be estimated consistently from data. Surprisingly, few studies investigate the properties of estimated generalized least squares (GLS) procedures when the structure of , is incorrectly identified and the parameters are inefficiently estimated. We compare the finite sample efficiencies of ordinary least squares (OLS), GLS and incorrect GLS (IGLS) estimators. We also prove new theorems establishing theoretical efficiency bounds for IGLS relative to GLS and OLS. Results from an exhaustive simulation study are used to evaluate the finite sample performance and to demonstrate the robustness of IGLS estimates vis-à-vis OLS and GLS estimates constructed for models with known and estimated (but correctly identified) ,. Some of our conclusions for finite samples differ from established asymptotic results. [source]

    Spurious Regression Under Broken-Trend Stationarity

    JOURNAL OF TIME SERIES ANALYSIS, Issue 5 2006
    Antonio E. Noriega
    C22 Abstract., We study the phenomenon of spurious regression between two random variables, when the generating mechanism of individual series is assumed to follow a stationary process around a trend with (possibly) multiple breaks in the level and slope of trend. We develop the relevant asymptotic theory and show that the phenomenon of spurious regression occurs independent of the structure assumed for the errors. In contrast to previous findings, the presence of a spurious relationship will be less severe when breaks are present in the generating mechanism of individual series. This is true whether the regression model includes a linear trend or not. Simulations confirm our asymptotic results, and reveal that in finite samples, the phenomenon of spurious regression is sensitive to the presence of a linear trend in the regression model and to the relative location of breaks within the sample. [source]

    A Generalized Portmanteau Test For Independence Of Two Infinite-Order Vector Autoregressive Series

    JOURNAL OF TIME SERIES ANALYSIS, Issue 4 2006
    Chafik Bouhaddioui
    Primary 62M10; secondary 62M15 Abstract., In many situations, we want to verify the existence of a relationship between multivariate time series. Here, we propose a semiparametric approach for testing the independence between two infinite-order vector autoregressive (VAR(,)) series, which is an extension of Hong's [Biometrika (1996c) vol. 83, 615,625] univariate results. We first filter each series by a finite-order autoregression and the test statistic is a standardized version of a weighted sum of quadratic forms in the residual cross-correlation matrices at all possible lags. The weights depend on a kernel function and on a truncation parameter. Using a result of Lewis and Reinsel [Journal of Multivariate Analysis (1985) Vol. 16, pp. 393,411], the asymptotic distribution of the test statistic is derived under the null hypothesis and its consistency is also established for a fixed alternative of serial cross-correlation of unknown form. Apart from standardization factors, the multivariate portmanteau statistic proposed by Bouhaddioui and Roy [Statistics and Probability Letters (2006) vol. 76, pp. 58,68] that takes into account a fixed number of lags can be viewed as a special case by using the truncated uniform kernel. However, many kernels lead to a greater power, as shown in an asymptotic power analysis and by a small simulation study in finite samples. A numerical example with real data is also presented. [source]

    Seasonal Unit Root Tests Under Structural Breaks,

    JOURNAL OF TIME SERIES ANALYSIS, Issue 1 2004
    Uwe Hassler
    C12; C22 Abstract., In this paper, several seasonal unit root tests are analysed in the context of structural breaks at known time and a new break corrected test is suggested. We show that the widely used HEGY test, as well as an LM variant thereof, are asymptotically robust to seasonal mean shifts of finite magnitude. In finite samples, however, experiments reveal that such tests suffer from severe size distortions and power reductions when breaks are present. Hence, a new break corrected LM test is proposed to overcome this problem. Importantly, the correction for seasonal mean shifts bears no consequence on the limiting distributions, thereby maintaining the legitimacy of canonical critical values. Moreover, although this test assumes a breakpoint a priori, it is robust in terms of misspecification of the time of the break. This asymptotic property is well reproduced in finite samples. Based on a Monte-Carlo study, our new test is compared with other procedures suggested in the literature and shown to hold superior finite sample properties. [source]

    Reducing size distortions of parametric stationarity tests

    JOURNAL OF TIME SERIES ANALYSIS, Issue 4 2003
    MARKKU LANNE
    The use of asymptotic critical values in stationarity tests against the alternative of a unit root process is known to lead to over-rejections in finite samples when the considered process is stationary but highly persistent. We claim that, in recent parametric tests, this is caused by estimation errors which result when the autoregressive parameters used to describe the short-run dynamics of the process are replaced by estimators. We suggest a modification that corrects for these errors. Simulation results show that the modified test works reasonably well when the persistence is moderate and there is no time trend in the model but it is less effective when the model contains a time trend. An empirical illustration with inflation rate data is provided. [source]

    On the Robustness of Unit Root Tests in the Presence of Double Unit Roots

    JOURNAL OF TIME SERIES ANALYSIS, Issue 2 2002
    NIELS HALDRUP
    We examine some of the consequences on commonly used unit root tests when the underlying series is integrated of order two rather than of order one. It turns out that standard augmented Dickey,Fuller type of tests for a single unit root have excessive density in the explosive region of the distribution. The lower (stationary) tail, however, will be virtually unaffected in the presence of double unit roots. On the other hand, the Phillips,Perron class of semi-parametric tests is shown to diverge to plus infinity asymptotically and thus favouring the explosive alternative. Numerical simulations are used to demonstrate the analytical results and some of the implications in finite samples. [source]

    A competitive coexistence principle?

    OIKOS, Issue 10 2009
    Cathy Neill
    Competitive exclusion , n species cannot coexist on fewer than n limiting resources in a constant and isolated environment , has been a central ecological principle for the past century. Since empirical studies cannot universally demonstrate exclusion, this principle has mainly relied on mathematical proofs. Here we investigate the predictions of a new approach to derive functional responses in consumer/resource systems. Models usually describe the temporal dynamics of consumer/resource systems at a macroscopic level , i.e. at the population level. Each model may be pictured as one time-dependent macroscopic trajectory. Each macroscopic trajectory is, however, the product of many individual fates and from combinatorial considerations can be realized in many different ways at the microscopic , or individual , level. Recently it has been shown that, in systems with large enough numbers of consumer individuals and resource items, one macroscopic trajectory can be realized in many more ways than any other at the individual , or microscopic , level. Therefore, if the temporal dynamics of an ecosystem are assumed to be the outcome of only statistical mechanics , that is, chance , a single trajectory is near-certain and can be described by deterministic equations. We argue that these equations can serve as a null to model consumer-resource dynamics, and show that any number of species can coexist on a single resource in a constant, isolated environment. Competition may result in relative rarity, which may entail exclusion in finite samples of discrete individuals, but exclusion is not systematic. Beyond the coexistence/exclusion outcome, our model also predicts that the relative abundance of any two species depends simply on the ratio of their competitive abilities as computed from , and only from , their intrinsic kinetic and stoichiometric parameters. [source]

    Testing for lack of dependence in the functional linear model

    THE CANADIAN JOURNAL OF STATISTICS, Issue 2 2008
    Piotr Kokoszka
    Abstract The authors consider the linear model Yn = ,Xn + ,n relating a functional response with explanatory variables. They propose a simple test of the nullity of , based on the principal component decomposition. The limiting distribution of their test statistic is chi-squared, but this distribution is also an excellent approximation in finite samples. The authors illustrate their method using data from terrestrial magnetic observatories. Un test d'absence de dépendance dans un modéle fonctionnel linéaire Les auteurs s'intéressent au modèle Yn = ,Xn + ,n linéaire , liant une variable réponse fonctionnelle à des variables explicatives. Ils proposent un test simple de nullité, de fondé sur la décomposition en composantes principales. La loi limite de leur statistique est une khi-deux, mais cette loi fournit aussi une excellente approximation à taille finie. Les auteurs illustrent leur méthode au moyen de données provenant d'observatoires du champ magnétique terrestre. [source]

    A wavelet solution to the spurious regression of fractionally differenced processes

    APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 3 2003
    Yanqin Fan
    Abstract In this paper we propose to overcome the problem of spurious regression between fractionally differenced processes by applying the discrete wavelet transform (DWT) to both processes and then estimating the regression in the wavelet domain. The DWT is known to approximately decorrelate heavily autocorrelated processes and, unlike applying a first difference filter, involves a recursive two-step filtering and downsampling procedure. We prove the asymptotic normality of the proposed estimator and demonstrate via simulation its efficacy in finite samples. Copyright © 2003 John Wiley & Sons, Ltd. [source]