Unknown Mean (unknown + mean)

Distribution by Scientific Domains


Selected Abstracts


Landslide events on the West Coast, South Island, 1867,2002

NEW ZEALAND GEOGRAPHER, Issue 1 2005
J. L. Benn
Abstract:, A new landslide event inventory based on a literature search has been compiled for the West Coast of New Zealand. Rainfall has been identified as the most frequent reported landslide generating mechanism by far, followed by other/unknown means, then earthquakes. Small-magnitude, high-frequency, rainfall-induced events have historically caused the most damage to property and infrastructure, with many of the region's highways and settlements being repeatedly affected by landslides. Since 1874, landslides have caused at least 36 fatalities in the region. More historical research is needed to fill chronological and geographical gaps in the record, and to complement scientific research. Such information is useful for hazard planning purposes. [source]


Marginal maximum likelihood estimation of item response theory (IRT) equating coefficients for the common-examinee design

JAPANESE PSYCHOLOGICAL RESEARCH, Issue 2 2001
Haruhiko Ogasawara
A method of estimating item response theory (IRT) equating coefficients by the common-examinee design with the assumption of the two-parameter logistic model is provided. The method uses the marginal maximum likelihood estimation, in which individual ability parameters in a common-examinee group are numerically integrated out. The abilities of the common examinees are assumed to follow a normal distribution but with an unknown mean and standard deviation on one of the two tests to be equated. The distribution parameters are jointly estimated with the equating coefficients. Further, the asymptotic standard errors of the estimates of the equating coefficients and the parameters for the ability distribution are given. Numerical examples are provided to show the accuracy of the method. [source]


The Limiting Density of Unit Root Test Statistics: A Unifying Technique

JOURNAL OF TIME SERIES ANALYSIS, Issue 3 2000
Mithat Gonen
In this note we introduce a simple principle to derive a constructive expression for the density of the limiting distribution, under the null hypothesis, of unit root statistics for an AR(1)-process in a variety of situations. We consider the case of unknown mean and reconsider the well-known situation where the mean is zero. For long-range dependent errors we indicate how the principle might apply again. We also show that in principle the method also works for a near unit root case. Weak convergence and subsequent Karhunen-Loeve expansion of the weak limit of the partial sum process of the errors plays an important role, along with the evaluation of a certain normal type integral with complex mean and variance. For independent and long range dependent errors this weak limit is ordinary and fractional Brownian motion respectively. AMS 1991 subject classification. Primary 62M10; secondary 62E20. [source]


Forecasting futures returns in the presence of price limits

THE JOURNAL OF FUTURES MARKETS, Issue 2 2005
Arie Harel
In a futures market with a daily price-limit rule, trading occurs only at prices within limits determined by the previous day's settlement price. Price limits are set in dollars but can be expressed as return limits. When the daily return limit is triggered, the true equilibrium futures return (and price) is unobservable. In such a market, investors may suffer from information loss if the return "moves the limit." Assuming normally distributed futures returns with unknown means but known volatilities, we develop a Bayesian forecasting model in the presence of return limits and provide some numerical predictions. Our innovation is the derivation of the predictive density for futures returns in the presence of return limits. 2005 Wiley Periodicals, Inc. Jrl Fut Mark 25:199,210, 2005 [source]


Maximum likelihood estimators of clock offset and skew under exponential delays

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 4 2009
Jun Li
Abstract Accurate clock synchronization is essential for many data network applications. Various algorithms for synchronizing clocks rely on estimators of the offset and skew parameters that describe the relation between times measured by two different clocks. Maximum likelihood estimation (MLE) of these parameters has previously been considered under the assumption of exponentially distributed network delays with known means. We derive the MLEs under the more common case of exponentially distributed network delays with unknown means and compare their mean-squared error properties to a recently proposed alternative estimator. We investigate the robustness of the derived MLE to the assumption of non-exponential network delays, and demonstrate the effectiveness of a bootstrap bias-correction technique. Copyright 2009 John Wiley & Sons, Ltd. [source]