scholarly journals Multivariate Skew t-Distribution: Asymptotics for Parameter Estimators and Extension to Skew t-Copula

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1059
Author(s):  
Tõnu Kollo ◽  
Meelis Käärik ◽  
Anne Selart
Keyword(s):  
Degrees Of Freedom ◽  
Data Modeling ◽  
Location Parameter ◽  
Asymptotic Distributions ◽  
Parameter Estimates ◽  
Parameter Distribution ◽  
Elliptical Distributions ◽  
T Copula ◽  
T Distribution ◽  
Parameter Estimators

Symmetric elliptical distributions have been intensively used in data modeling and robustness studies. The area of applications was considerably widened after transforming elliptical distributions into the skew elliptical ones that preserve several good properties of the corresponding symmetric distributions and increase possibilities of data modeling. We consider three-parameter p-variate skew t-distribution where p-vector μ is the location parameter, Σ:p×p is the positive definite scale parameter, p-vector α is the skewness or shape parameter, and the number of degrees of freedom ν is fixed. Special attention is paid to the two-parameter distribution when μ=0 that is useful for construction of the skew t-copula. Expressions of the parameters are presented through the moments and parameter estimates are found by the method of moments. Asymptotic normality is established for the estimators of Σ and α. Convergence to the asymptotic distributions is examined in simulation experiments.

scholarly journals Kumaraswamy Generalized Power Lomax Distributionand Its Applications

Stats ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 28-45
Author(s):  
Vasili B.V. Nagarjuna ◽  
R. Vishnu Vardhan ◽  
Christophe Chesneau
Keyword(s):  
Hazard Rate ◽  
Real Data ◽  
Rate Function ◽  
Maximum Likelihood Estimates ◽  
Parameter Estimates ◽  
Parameter Distribution ◽  
Data Sets ◽  
Lomax Distribution ◽  
Entropy Measures ◽  
Modeling Behavior

In this paper, a new five-parameter distribution is proposed using the functionalities of the Kumaraswamy generalized family of distributions and the features of the power Lomax distribution. It is named as Kumaraswamy generalized power Lomax distribution. In a first approach, we derive its main probability and reliability functions, with a visualization of its modeling behavior by considering different parameter combinations. As prime quality, the corresponding hazard rate function is very flexible; it possesses decreasing, increasing and inverted (upside-down) bathtub shapes. Also, decreasing-increasing-decreasing shapes are nicely observed. Some important characteristics of the Kumaraswamy generalized power Lomax distribution are derived, including moments, entropy measures and order statistics. The second approach is statistical. The maximum likelihood estimates of the parameters are described and a brief simulation study shows their effectiveness. Two real data sets are taken to show how the proposed distribution can be applied concretely; parameter estimates are obtained and fitting comparisons are performed with other well-established Lomax based distributions. The Kumaraswamy generalized power Lomax distribution turns out to be best by capturing fine details in the structure of the data considered.

3 Power and sample size for ABE in the 2× 2 design Here we give formulae for the calculation of the power of the TOST procedure assuming that there are in total n subjects in the 2× 2 trial. Suppose that the null hypotheses given below are to be tested using a 100(1 − α)% two-sided confidence interval and a power of (1 − β) is required to reject these hypotheses when they are false. H :µ ≤− ln 1.25 H :µ ≥ ln 1.25. Let us define x to be a random variable that has a noncentral t-distribution with df degrees of freedom and noncentrality parameter nc, i.e., x ∼ t(df,nc). The cumulative distribution function of x is defined as CDF(t,df,nc) = Pr(x≤ t). Assume that the power is to be calculated using log(AUC). If σ is the common within-subject variance for T and R for log(AUC), and n/2 subjects are allocated to each of the sequences RT and TR, then 1−β = CDF(t , df,nc )−CDF(t , df,nc ) (7.1) where √ n(log(µ )− log(0.8)) nc = 2σ √ n(log(µ )− log(1.25)) nc = 2σ √ (n− 2)(nc −nc = ) df 2t and t is the 100(1 − α)% point of the central t-distribution on n− 2 degrees of freedom. Some SAS code to calculate the power for an ABE 2 × 2 trial is given below, where the required input variables are α, σ value of the ratio µ and n, the total number of subjects in the trial.

2003 ◽  
pp. 361-361
Keyword(s):  
Distribution Function ◽  
Confidence Interval ◽  
Degrees Of Freedom ◽  
Random Variable ◽  
Noncentrality Parameter ◽  
Cumulative Distribution ◽  
Total N ◽  
The Common ◽  
Input Variables ◽  
T Distribution

scholarly journals Validation of theα-μModel of the Power Spectral Density of GPS Ionospheric Amplitude Scintillation

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Kelias Oliveira ◽  
Alison de Oliveira Moraes ◽  
Emanoel Costa ◽  
Marcio Tadeu de Assis Honorato Muella ◽  
Eurico Rodrigues de Paula ◽  
...  
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Degrees Of Freedom ◽  
Solar Flux ◽  
Global Navigation Satellite Systems ◽  
Ionospheric Scintillation ◽  
Parameter Distribution ◽  
Satellite Systems ◽  
Amplitude Scintillation ◽  
Power Spectral

Theα-μmodel has become widely used in statistical analyses of radio channels, due to the flexibility provided by its two degrees of freedom. Among several applications, it has been used in the characterization of low-latitude amplitude scintillation, which frequently occurs during the nighttime of particular seasons of high solar flux years, affecting radio signals that propagate through the ionosphere. Depending on temporal and spatial distributions, ionospheric scintillation may cause availability and precision problems to users of global navigation satellite systems. The present work initially stresses the importance of the flexibility provided byα-μmodel in comparison with the limitations of a single-parameter distribution for the representation of first-order statistics of amplitude scintillation. Next, it focuses on the statistical evaluation of the power spectral density of ionospheric amplitude scintillation. The formulation based on theα-μmodel is developed and validated using experimental data obtained in São José dos Campos (23.1°S; 45.8°W; dip latitude 17.3°S), Brazil, located near the southern crest of the ionospheric equatorial ionization anomaly. These data were collected between December 2001 and January 2002, a period of high solar flux conditions. The results show that the proposed model fits power spectral densities estimated from field data quite well.

Adaptive Mismatch Compensation for Rate Integrating Vibratory Gyroscopes With Improved Convergence Rate

2014 ◽  
Author(s):  
Fu Zhang ◽  
Ehsan Keikha ◽  
Behrooz Shahsavari ◽  
Roberto Horowitz
Keyword(s):  
Convergence Rate ◽  
Degrees Of Freedom ◽  
Rotation Angle ◽  
Least Square ◽  
Parameter Estimates ◽  
Unknown Parameters ◽  
Least Square Estimator ◽  
Vibratory Gyroscopes ◽  
Adaptive Compensator ◽  
Simulation Results

This paper presents an online adaptive algorithm to compensate damping and stiffness frequency mismatches in rate integrating Coriolis Vibratory Gyroscopes (CVGs). The proposed adaptive compensator consists of a least square estimator that estimates the damping and frequency mismatches, and an online compensator that corrects the mismatches. In order to improve the adaptive compensator’s convergence rate, we introduce a calibration phase where we identify relations between the unknown parameters (i.e. mismatches, rotation rate and rotation angle). Calibration results show that the unknown parameters lie on a hyperplane. When the gyro is in operation, we project parameters estimated from the least square estimator onto the hyperplane. The projection will reduce the degrees of freedom in parameter estimates, thus guaranteeing persistence of excitation and improving convergence rate. Simulation results show that utilization of the projection method will drastically improve convergence rate of the least square estimator and improve gyro performance.

scholarly journals Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments

2004 ◽  
Vol 3 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Gordon K Smyth
Keyword(s):  
Differential Expression ◽  
Degrees Of Freedom ◽  
Empirical Bayes ◽  
Linear Models ◽  
Single Channel ◽  
Posterior Odds ◽  
Microarray Experiments ◽  
Study Results ◽  
Standard Deviations ◽  
T Distribution

The problem of identifying differentially expressed genes in designed microarray experiments is considered. Lonnstedt and Speed (2002) derived an expression for the posterior odds of differential expression in a replicated two-color experiment using a simple hierarchical parametric model. The purpose of this paper is to develop the hierarchical model of Lonnstedt and Speed (2002) into a practical approach for general microarray experiments with arbitrary numbers of treatments and RNA samples. The model is reset in the context of general linear models with arbitrary coefficients and contrasts of interest. The approach applies equally well to both single channel and two color microarray experiments. Consistent, closed form estimators are derived for the hyperparameters in the model. The estimators proposed have robust behavior even for small numbers of arrays and allow for incomplete data arising from spot filtering or spot quality weights. The posterior odds statistic is reformulated in terms of a moderated t-statistic in which posterior residual standard deviations are used in place of ordinary standard deviations. The empirical Bayes approach is equivalent to shrinkage of the estimated sample variances towards a pooled estimate, resulting in far more stable inference when the number of arrays is small. The use of moderated t-statistics has the advantage over the posterior odds that the number of hyperparameters which need to estimated is reduced; in particular, knowledge of the non-null prior for the fold changes are not required. The moderated t-statistic is shown to follow a t-distribution with augmented degrees of freedom. The moderated t inferential approach extends to accommodate tests of composite null hypotheses through the use of moderated F-statistics. The performance of the methods is demonstrated in a simulation study. Results are presented for two publicly available data sets.

Sign in / Sign up

Export Citation Format

Share Document