Sample correlations of infinite variance time series models: An empirical and theoretical study.
Sample -copula of order
In this paper we analyze the construction of -copulas including the ideas of Cuculescu and Theodorescu [5], Fredricks et al. [15], Mikusiński and Taylor [25] and Trutschnig and Fernández-Sánchez [33]. Some of these methods use iterative procedures to construct copulas with fractal supports. The main part of this paper is given in Section 3, where we introduce the sample -copula of order with , the central idea is to use the above methodologies to construct a new copula based on a sample. The...
Sample density as the function-estimate of population's distribution
Sample partitioning estimation for ergodic diffusions: application to Ornstein-Uhlenbeck diffusion
When a diffusion is ergodic its transition density converges to its invariant density, see Durrett (1998). This convergence enabled us to introduce a sample partitioning technique that gives in each sub-sample, maximum likelihood estimators. The averages of these being a natural choice as estimators. To compare our estimators with the optimal we obtained from martingale estimating functions, see Sørensen (1998), we used the Ornstein-Uhlenbeck process for which exact simulations can be carried out....
Sample size and tolerance limits.
Several new criteria are proposed for the determination of suitable sample size for assessing the statistical tolerance limits. The application of the criteria is illustrated on the solution of some problems from the theory of errors and theory of reliability.
Sample size for testing homogeneity of two a priori dependent binomial populations using the Bayesian approach.
Sample Size, Parameter Rates and Contiguity - The i.n.n.i.d. Case
Sampling design variance estimation of small area estimators in the Spanish Labour Force survey.
The main goal of this paper is to investigate how to estimate sampling design variances of modelbased and model-assisted small area estimators in a complex survey sampling setup. For this purpose the Spanish Labour Force Survey is considered. Sample and aggregated data are taken from the Canary Islands in the second trimester of 2003 in order to obtain some small area estimators of ILO unemployment totals. Several problems arising from the application of standard small area estimation procedures...
Sampling inference, Bayes' inference and robustness in the advancement of learning.
Scientific learning is seen as an iterative process employing Criticism and Estimation. Sampling theory use of predictive distributions for model criticism is examined and also the implications for significance tests and the theory of precise measurement. Normal theory examples and ridge estimates are considered. Predictive checking functions for transformation, serial correlation, and bad values are reviewed as is their relation with Bayesian options. Robustness is seen from a Bayesian view point...
Sampling properties of estimators of nucleotide diversity at discovered SNP sites
SNP sites are generally discovered by sequencing regions of the human genome in a limited number of individuals. This may leave SNP sites present in the region, but containing rare mutant nucleotides, undetected. Consequently, estimates of nucleotide diversity obtained from assays of detected SNP sites are biased. In this research we present a statistical model of the SNP discovery process, which is used to evaluate the extent of this bias. This model involves the symmetric Beta distribution of...
Sampling Studies on the Behrens-Fisher Problem.
SAR image filtering in wavelet domain by subband depended shrink.
Scale space methods for analysis of type 2 diabetes patients' blood glucose values.
Scaling of model approximation errors and expected entropy distances
We compute the expected value of the Kullback-Leibler divergence of various fundamental statistical models with respect to Dirichlet priors. For the uniform prior, the expected divergence of any model containing the uniform distribution is bounded by a constant . For the models that we consider this bound is approached as the cardinality of the sample space tends to infinity, if the model dimension remains relatively small. For Dirichlet priors with reasonable concentration parameters the expected...
Scatter halfspace depth: Geometric insights
Scatter halfspace depth is a statistical tool that allows one to quantify the fitness of a candidate covariance matrix with respect to the scatter structure of a probability distribution. The depth enables simultaneous robust estimation of location and scatter, and nonparametric inference on these. A handful of remarks on the definition and the properties of the scatter halfspace depth are provided. It is argued that the currently used notion of this depth is well suited especially for symmetric...
Scenario generation with distribution functions and correlations
In this paper, we present a method for generating scenarios for two-stage stochastic programs, using multivariate distributions specified by their marginal distributions and the correlation matrix. The margins are described by their cumulative distribution functions and we allow each margin to be of different type. We demonstrate the method on a model from stochastic service network design and show that it improves the stability of the scenario-generation process, compared to both sampling and a...
Schätzbare Parameterfunktionen in der Stichprobentheorie.
Schätzen der Kovarianzdichte stationärer Punktprozesse aus Zählungen.
Schémas de discrétisation anticipatifs et estimation du paramètre de dérive d'une diffusion
Let YT = (Yt)t∈[0,T] be a real ergodic diffusion process which drift depends on an unkown parameter . Our aim is to estimate θ0 from a discrete observation of the process YT, (Ykδ)k=0,n, for a fixed and small δ, as T = nδ goes to infinity. For that purpose, we adapt the Generalized Method of Moments (see Hansen) to the anticipative and approximate discrete-time trapezoidal scheme, and then to Simpson's. Under some general assumptions, the trapezoidal scheme (respectively Simpson's scheme)...
Schémas de discrétisation anticipatifs et estimation du paramètre de dérive d'une diffusion