Model-calibration estimation of the distribution function using nonparametric regression

Nonparametric regression has only recently been employed in the estimation of finite population parameters in a model-assisted framework. This paper proposes a new calibration estimator for the distribution function using nonparametric methods to obtain the fitted values on which to calibrate. The proposed estimator is a genuine distribution function that presents several attractive features. In terms of relative efficiency and relative bias, the behaviour of the proposed estimator is compared to other known estimators in a limited simulation study on real populations.     

Minimax invariant estimation of a continuous distribution function under entropy loss

For the invariant decision problem of estimating a continuous distribution function F with two entropy loss functions, it is proved that the best invariant estimators d ₀ exist and are the same as the best invariant estimator of a continuous distribution function under the squared error loss function L (F, d)=∫|F (t) −d (t) |² dF (t). They are minimax for any sample size n≥1.     

Kernel estimation of a smooth distribution function based on censored data

The problem of estimating a smooth distribution functionF at a pointτ based on randomly right censored data is treated under certain smoothness conditions onF. The asymptotic performance of a certain class of kernel estimators is compared to that of the Kaplan-Meier estimator ofF(τ). It is shown that the relative deficiency of the Kaplan-Meier estimator ofF(τ) with respect to the appropriately chosen kernel type estimator tends to infinity as the sample sizen increases to infinity. Strong uniform consistency and the weak convergence of the normalized process are also proved. Research Surported in part by NIH grant 1R01GM28405.     

The distribution function of a statistic for testing the equality of scale parameters in two gamma populations

Summary The exact distribution function of the ratio of two sums of gamma variates is derived in this paper. The result applies to ratios of quadratic forms and to a statistic used for testing the equality of scale parameters in two gamma populations.     

Minimax estimation of a cumulative distribution function by converting to a parametric problem

Let X = (X ₁,...,X _n ) be a sample from an unknown cumulative distribution function F defined on the real line . The problem of estimating the cumulative distribution function F is considered using a decision theoretic approach. No assumptions are imposed on the unknown function F. A general method of finding a minimax estimator d(t;X) of F under the loss function of a general form is presented. The method of solution is based on converting the nonparametric problem of searching for minimax estimators of a distribution function to the parametric problem of searching for minimax estimators of the probability of success for a binomial distribution. The solution uses also the completeness property of the class of monotone decision procedures in a monotone decision problem. Some special cases of the underlying problem are considered in the situation when the loss function in the nonparametric problem is defined by a weighted squared, LINEX or a weighted absolute error.     

Nonparametric estimation of distribution functions

Summary Using Silverman and Young’s (1987) idea of rescaling a rescaled smoothed empirical distribution function is defined and investigated when the smoothing parameter depends on the data. The rescaled smoothed estimator is shown to be often better than the commonly used ordinary smoothed estimator.     

Interval estimation for the two-parameter exponential distribution under progressive censoring

The interval estimation of the scale parameter and the joint confidence region of the parameters of two-parameter exponential distribution under Type II progressive censoring is proposed. In addition, the simulation study for the performance of all proposed pivotal quantities is done in this paper. The criteria of minimum confidence length and minimum confidence area are used to obtain the optimal estimation. The predictive intervals of the future observation and the future interarrival times based on the Type II progressive censored sample are also provided. One biometrical example is also given to illustrate the proposed methods.     

Efficient estimation of income distribution parameters

The parameters of several families of distributions are estimated by means of minimum χ²; use is made of random samples taken from Dutch income-earning groups in 1973. The numerical search routine used, is the Complex method due to Box. The χ² function is evaluated by standard numerical integration procedures. The lognormal and the Gamma families are rejected because of a poor fit. The log t and the log Pearson IV families are introduced. This results in a considerable improvement of χ² critical levels. The generalized Gamma and the Champernowne function describe the income distribution reasonably well in some cases.     

Simultaneous estimation of hazard rates of several exponential populations

Suppose independent random samples are drawn from k (2) populations with a common location parameter and unequal scale parameters. We consider the problem of estimating simultaneously the hazard rates of these populations. The analogues of the maximum likelihood (ML), uniformly minimum variance unbiased (UMVU) and the best scale equivariant (BSE) estimators for the one population case are improved using Rao‐Blackwellization. The improved version of the BSE estimator is shown to be the best among these estimators. Finally, a class of estimators that dominates this improved estimator is obtained using the differential inequality approach.     

Agglomerative tendencies in the distribution of populations

"The paper explores the role of accessibility to mutual contacts as an agglomeration force in the spatial distribution of population. The uniqueness conditions for the equilibrium solutions are analyzed in the static case, with the aid of mathematical programming embedding properties. A dynamic version in continuous time is then built, and conditions for instability of a globally stable equilibrium and appearance of multiple locally stable and unstable equilibria are stated. Finally, some implications for the geographical structure are discussed."     

Minimax estimation for the bounded mean of a bivariate normal distribution

A bivariate normal distribution is considered whose mean lies in an equilateral triangle. We show by a convexity argument that the three point prior having mass 1/3 at each of the edges is least favourable if the length of a side of the equilateral triangle is less than or equal to . Thus the corresponding Bayes estimator is minimax in that case. Numerical studies are given as well.     

Transforms for finding probabilities and variate values of a distribution function in tables of a related distribution function

Het verband tussen enige bedkende verdelingen.
In dit artikel worden enkele schema's gegeven voor het verband tussen een aantal van de meest bekende verdelingen. Bovendien wordt een aantal andere relaties besproken. He doel van dit artikel is drieledig. Ten eerste kunnen statistische afdelingen, die in het bezit zijn van een uitgebreide tabellenverzamelign de gegeven transformaties gebruiken om bestaande tabellen uit andere tabellen te vervolledigen ten dienste van bepaalde praktische toepassingen. Ten tweede kunnen statistici, die niet in het bezit van volledige tabellenverzamelingen zijn, ontbreakende tabellen vervanden tussen deze verdelingen leiden tot meer inzicht in bepaalde toetsings-methoden. Dit laatste punt wordt niet uitvoerig uitgewerkt.
Par. 1. geeft een inleiding en overzicht; par. 2 vermeldt de gebruikte symbolen en verwijst naar bekende tabellenverzamelingen; par. 3 en 4 geven schematisch de transformaties weer; par. 5 geeft een schematisch overzicht van de relaties; par. 6 geeft een aantal numerieke voorbeelden; in de appendix worden de wiskundige formules van de verdelingen vermeld.
Afleidingen worden niet gegeven.     

Consistent likelihood-based estimation of a star-shaped distribution

We consider the classical problem of nonparametrically estimating a star-shaped distribution, i.e., a distribution function F on [0,∞) with the property that F(u)/u is nondecreasing on the set {u : F(u) < 1}. This problem is intriguing because of the fact that a well defined maximum likelihood estimator (MLE) exists, but this MLE is inconsistent. In this paper, we argue that the likelihood that is commonly used in this context is somewhat unnatural and propose another, so called ‘smoothed likelihood’. However, also the resulting MLE turns out to be inconsistent. We show that more serious smoothing of the likelihood yields consistent estimators in this model.     

On some noncentral distribution problems for the mixture of two normal populations

Summary The null density of the multiple correlation coefficient when sampling from a mixture of two normal populations has been derived by Srivastava (1983). However, he does not express this density in a rather standard form. The present paper obtains the nonnull densities of the multiple correlation coefficient and the partial correlation coefficient in a rather standard form when sampling from a mixture of two normal populations.     

Information criteria for impulse response function matching estimation of DSGE models

We propose new information criteria for impulse response function matching estimators (IRFMEs). These estimators yield sampling distributions of the structural parameters of dynamic stochastic general equilibrium (DSGE) models by minimizing the distance between sample and theoretical impulse responses. First, we propose an information criterion to select only the responses that produce consistent estimates of the true but unknown structural parameters: the Valid Impulse Response Selection Criterion (VIRSC). The criterion is especially useful for mis-specified models. Second, we propose a criterion to select the impulse responses that are most informative about DSGE model parameters: the Relevant Impulse Response Selection Criterion (RIRSC). These criteria can be used in combination to select the subset of valid impulse response functions with minimal dimension that yields asymptotically efficient estimators. The criteria are general enough to apply to impulse responses estimated by VARs, local projections, and simulation methods. We show that the use of our criteria significantly affects estimates and inference about key parameters of two well-known new Keynesian DSGE models. Monte Carlo evidence indicates that the criteria yield gains in terms of finite sample bias as well as offering tests statistics whose behavior is better approximated by the first order asymptotic theory. Thus, our criteria improve existing methods used to implement IRFMEs.     

Bayesian parameter and reliability estimation for a bivariate exponential distribution parallel sampling

The present investigation is concerned with deriving Bayesian statistical inferences for the bivariate exponential (BVE) distribution of Marshall and Olkin (1967) applied as a failure model for a two-component parallel system. In this paper joint posterior distributions for the BVE parameters and marginal posterior densities for individual parameters are developed. The posterior distributions are derived for the case of informative prior knowledge. Bayesian estimators for the BVE parameters and the corresponding reliability are derived in a closed form. Bayesian approximated credibility intervals (‘confidence’ intervals) for parameters are derived by utilizing a gamma approximation to the marginal posterior densities.     

Parameter estimation for a bivariate lifetime distribution in reliability with multivariate extensions

Bivariate lifetime distributions are considered which describe physically motivated dependencies like those proposed by Freund (1961) and Marshall and Olkin (1967a). Such distributions arise in reliability problems with two-component systems. Generalizations of some previous models are investigated and the maximum likelihood estimates for a combined bivariate exponential distribution are given. The case of dependent random censorship is considered in connection with two-component series systems. Some simulations show how censorship affects the parameter estimates.     

Complete sufficiency and maximum likelihood estimation for the two-parameter negative binomial distribution

The difficult estimation problem associated with the two-parameter negative binomial distribution is discussed. The order statistic is shown to be minimal sufficient but not complete. It is proven that there is at least one maximum likelihood estimator of the parameterk when the second sample moment is greater than the sample mean. Contours and three-dimensional graphs of the natural logarithm of the likelihood function provide further insight into the estimation problem.     

Nonparametric estimation of a hedonic price function

Rosen's ( 1974 ) theory of hedonic prices is implemented econometrically using recently developed nonparametric techniques to examine the influence of qualitative factors on the price of a house. Our ability to smooth categorical variables leads to greater generalization in the valuation process and provides a canvas for interactions between categorical and continuous variables that is difficult to exploit in parametric and semiparametric models. This is illustrated with a replication of a previously used partially linear model specification. Copyright © 2007 John Wiley & Sons, Ltd.     

Money demand function estimation by nonlinear cointegration

Conventionally, the money demand function is estimated using a regression of the logarithm of money demand on either the interest rate or the logarithm of the interest rate. This equation is presumed to be a cointegrating regression. In this paper, we aim to combine the logarithmic specification, which models the liquidity trap better than a linear model, with the assumption that the interest rate itself is an integrated process. The proposed technique is robust to serial correlation in the errors. For the USA, our new technique results in larger coefficient estimates than previous research suggested, and produces superior out‐of‐sample prediction. Copyright © 2007 John Wiley & Sons, Ltd.