Estimation of error variance in one-way random model

A preliminary-test estimator for the error variance in the one-way random model is considered. The optimum levels of significance for the preliminary test are obtained based on a regret function. A pooling procedure for estimating the error variance, based on weighting functions, is also considered. A comparison of these estimators is made.     

An alternative model for the analysis of variance with random effects

The standard model for the analysis of variance with random effects implies, for the case of two independent variables, that single effects must be tested not against the error, but against the interaction mean squares. This causes, in comparison with the fixed effects AV, a considerable loss of test power, particularly for the 2 × 2 table. An alternative modelling of the interaction effect is proposed which completely avoids the loss of power.     

The analysis of unbalanced linear models with variance components

Statistical inference for fixed effects, random effects and components of variance in an unbalanced linear model with variance components will be discussed. Variance components will be estimated by Restricted Maximum Likelihood. Iterative procedures for computing the estimates, such as Fisher scoring and the EM-algorithm, are described.     

The random coefficients logit model is identified

The random coefficients multinomial choice logit model, also known as the mixed logit, has been widely used in empirical choice analysis for the last thirty years. We prove that the distribution of random coefficients in the multinomial logit model is nonparametrically identified. Our approach requires variation in product characteristics only locally and does not rely on the special regressors with large supports used in related papers. One of our two identification arguments is constructive. Both approaches may be applied to other choice models with random coefficients.     

Asymptotic behavior for S-estimators in random design linear model with long-range-dependent errors

The asymptotic behavior of S-estimators in a random design linear model with long-range-dependent Gaussian errors is considered. It turns out that the S-estimators of regression parameter and error variance are strongly consistent under mild conditions. Furthermore, the asymptotic distribution of the S-estimator of regression parameter is normal if the design vectors are i.i.d. and is non-normal if the design vectors are long-range dependent Gaussian vectors. We also show that the asymptotic distribution of S-estimator of the error variance is non-normal since the errors are long-range dependent. Supported by National Natural Science Foundation of China (Grant No. 10571159) and Specialized Research Fund for the Doctor Program of Higher Education (Grant No. 2002335090).     

The best quadratic estimator of the residual variance in regression analysis

De beste kwadratische schattingsfunctie van de storingsvariantie in regressie-analyse.
Dit artikel handelt over de schatting van de variantie σ² van de storingen in de regressieanalyse onder klassieke veronderstellingen: niet-stochastische waarden aangenomen door de verklarende variabelen en normaliteit, onafhankelijkheid en homoskedasticiteit van de storingen. Bekend is dat de schatting volgens maximale aannemelijkheid neerkomt op net bepalen van de kwadratensom van de volgens kleinste-kwadraten geschatte storingen en deling door T (het aantal waarne-mingen); voorts, dat de schatting die minimale variantie heeft binnen de klasse van schattingsfuncties die zuiver zijn en kwadratisch in de afhankelijke variabele (de beste zuivere kwadratische schattingsfunctie) gevonden wordt door genoemde kwadratensom te delen door T–A, waarbij λ het aantal te schatten coëfficiënten is [d.w.z. het aantal verklarende variabelen (+ 1 indien een constante term aanwezig is)]. Hier wordt aangetoond, dat de schattingsfunctie van σ² die een minimaal tweede moment heeft binnen de klasse van schattingsfuncties die kwadratisch zijn in de afhankelijke variabele (de beste kwadratische schattingsfunctie) gevonden wordt door de kwadratensom van de volgens kleinste kwadraten geschatte storingen te delen door T–Λ+ 2.     

The general linear model analysis of variance: its relevance to transportation planning and research†

The general linear model analysis of variance (GLANOVA) is discussed to show its relevance to several transportation planning and research topics. Two specific analytical procedures, an analysis of variance for multiway tables with unequal cell frequencies and an analysis of covariance, are presented. The potential applicability of these GLANOVA procedures to trip generation, mode split and market segmentation in transportation planning and research contexts is mentioned. It is concluded that further examination of GLANOVA techniques, both on empirical and analytical levels, will be fruitful for research studies. However, theoretical sophistication and lack of convenient computer programs which are compatible with transportation planning software make GLANOVA inappropriate for use in normal planning studies.     

Bootstrap of a linear model with AR-error structure

We consider a linear model with autoregressive error structure. It is shown that with probability one the distribution of the two-stage GLS estimator admits a bootstrap approximation. In a simulation study it is demonstrated that the bootstrap outperforms the normal approximation if the innovation variables are heavily correlated.     

Inferences about the parameters of a time series model with changing variance

Summary We consider the problem of making inferences about the parameters of a time series model when there is the possibility of a discrete variance change at an unknown time point. For this we obtain the posterior distributions of the parameters and of the variance ratio.     

On classes of estimators of the variance function of a linear estimator

Summary The variance function of a linear estimator can be expressed into a quadratic form. The present paper presents classes of estimators of this quadratic form along the lines implicitly suggested byHorvitz andThompson [1952] while formulating the classes of linear estimators. Accordingly it is noted that there exist nine principal classes of estimators out of which one principal class is examined in detail. Furthermore to illustrate the theory an example is considered where the expression for a unique estimator variance of the best estimator in theT ₁ class is derived.     

Estimating the codifference function of linear time series models with infinite variance

We consider the codifference and the normalized codifference function as dependence measures for stationary processes. Based on the empirical characteristic function, we propose estimators of the codifference and the normalized codifference function. We show consistency of the proposed estimators, where the underlying model is the ARMA with symmetric α-stable innovations, 0 < α ≤ 2. In addition, we derive their limiting distribution. We present a simulation study showing the dependence of the estimator on certain design parameters. Finally, we provide an empirical example using some stocks from Indonesia Stock Exchange.     

The structure of ARMA solutions to a general linear model with rational expectations

A parameterc characterization is provided for the complete class of stationary and non-stationary ARMA solutions, generated by the fundamental exogenous innovation, to a general linear univariate model with rational expectations. The dimension of the solution space of ARMA parameters is given and shown to be generically equal to the maximum period forward over which expectations are formed. The validity of deleting common factors in non-stationary ARMA processes is discussed, and specific solution choices often recommended are shown to be ‘common factor’ solutions.     

Equal-treatment policy in a random search model with taste discrimination

We consider a search model of the labor market with two types of equally productive workers and two types of firms, discriminators and non-discriminators. Without policy intervention, there is wage dispersion between and within the two worker groups, but all wage differences become negligible when the taste for discrimination is small. We analyze the effect of an equal-pay policy, both in combination with affirmative action and without. When equal opportunity of hiring cannot be enforced, wage dispersion increases and wages for minority workers fall substantially relative to laissez faire. Sometimes also the wage gap between worker groups widens in response to the policy.     

Estimating the variance of a finite population under a superpopulation model

The problem of deriving optimum sampling strategies for estimating the variance of finite population has been considered under some super-population set up.     

Bayesian estimation of a random coefficient model

This paper carries out a Bayesian analysis of the Hildreth-Houck (1968) random coefficient model and applies it to some cross-section production function data. Posterior distributions for mean coefficients, actual coefficients, variances and variance ratios are derived. The variance ratio posteriors are largely uninformative but they do lead to relatively informative densities on the variances, and the problem of negative variance estimates, obtained with previous techniques, is overcome. Posterior densities for the mean coefficients are not extremely sensitive to the variance ratios.     

Analytically pricing variance and volatility swaps under a Markov-modulated model with liquidity risks

This paper determines strike prices of discretely sampled variance/volatility swaps taking into account stochastic liquidity risks and the switching of economic conditions. We adopt nonlinear regime switching volatility to reflect how asset prices are affected by economic cycles, and market prices of assets are discounted according to the level of market liquidity. We then establish a risk-neutral measure under regime switching Esscher transform, so that analytical valuation of variance/volatility swaps can be completed based on the closed-form forward characteristic function. The limiting behavior of discretely sampled variance/volatility swaps is also considered through the investigation of pricing continuously sampled variance/volatility swaps. Finally, based on the results from numerical implementation, we confirm that the new model is very flexible in reflecting different influence associated with common real market observations.     

Asymptotically optimal nonparametric one- and two-way analysis of variance tests for the log linear model under censoring

We considerr ×c populations with failure ratesλ _ij(t) satisfying the condition

The variance of the discrepancy distribution of rounding procedures,and sums of uniform random variables

When \(\ell \) probabilities are rounded to integer multiples of a given accuracy n, the sum of the numerators may deviate from n by a nonzero discrepancy. It is proved that, for large accuracies \(n \rightarrow \infty \), the limiting discrepancy distribution has variance \(\ell /12\). The relation to the uniform distribution over the interval \([-1/2, 1/2]\), whose variance is 1 / 12, is explored in detail.