A test for the mean vector in large dimension and small samples

In this paper, we consider the problem of testing the mean vector in the multivariate setting where the dimension p is greater than the sample size n, namely a large p and small n problem. We propose a new scalar transform invariant test and show the asymptotic null distribution and power of the proposed test under weaker conditions than Srivastava (2009). We also present numerical studies including simulations and a real example of microarray data with comparison to existing tests developed for a large p and small n problem.     

Scheduling and compression for a multiple robot assembly workcell

A scheduling and compression strategy is presented for a multiple robot assembly workcell. Based on dynamic programming, the algorithm gives an initial assembly sequence for a two- or three-robot cell with a common resource or dedicated ones. The initial assembly sequence is computed efficiently, with computational complexity of being O (log n/k) where n is the number of elements in k resources. Then the initial sequence is further compressed towards the time-optimal assemblysequence. Precedence constraints in assembly are considered. The scheme is tested using computer simulations and some test results are presented. For the problems solved (typically involving 20 elements), the computational times were less than 5 seconds in an Apollo 300 workstation.     

One–sided simulataneous prediction intervals for the order statistics of a future samples from an exponential distribution

Assume that a sample is available from a population having an exponential distribution, and that l Future sample are to be taken from the same population. This paper provides a formula for the same population. This paper provides a formula for computing a one–sided lower simulataneous prediction limit which is to be below the (k_i ? m_i + 1) –st order statistics of a future sample of size k_i for the i = 1,…,2, hased on the sample mean of a past sample. Tables for factors for one–sided lower simultaneous predicition limits are provided. Such limits are of practical importance in determining acceptance criteria and predicting system survival times.     

Testing,One, Two,Three, Testing: An Attorney Perspective

SUMMARY

This article addresses the attorney perspective on the use of psychometric testing in the context of family law child custody evaluations. Although attorneys, judicial officers and child custody evaluators “use” psychometric testing in family law matters, the ways in which each discipline uses the information gleaned from the results of these tests as administered to child custody litigants can differ substantially. This article distinguishes the attorney perspective from the mental health perspective in the use of the results of such tests.     

Selecting Regressors for Prediction Using PRESS and White t Statistics

It is shown that selecting regressors based on the prediction error sum of squares is asymptotically related to hypothesis testing with White's (1980) heteroscedasticity-consistent covariance matrix. A simulation experiment suggests that this asymptotic relation may be useful. Illustrative examples are also given.     

Testing for Slope Heterogeneity Bias in Panel Data Models

ABSTRACT

Standard econometric methods can overlook individual heterogeneity in empirical work, generating inconsistent parameter estimates in panel data models. We propose the use of methods that allow researchers to easily identify, quantify, and address estimation issues arising from individual slope heterogeneity. We first characterize the bias in the standard fixed effects estimator when the true econometric model allows for heterogeneous slope coefficients. We then introduce a new test to check whether the fixed effects estimation is subject to heterogeneity bias. The procedure tests the population moment conditions required for fixed effects to consistently estimate the relevant parameters in the model. We establish the limiting distribution of the test and show that it is very simple to implement in practice. Examining firm investment models to showcase our approach, we show that heterogeneity bias-robust methods identify cash flow as a more important driver of investment than previously reported. Our study demonstrates analytically, via simulations, and empirically the importance of carefully accounting for individual specific slope heterogeneity in drawing conclusions about economic behavior.     

Estimation and Testing of Learning Curves

This article describes a new approach to learning curve estimation. Our approach is to formulate statistical procedures that conform to alternative learning curve theories. This leads to the development of nonlinear statistical models of the learning curves. For the three data sets analyzed, autocorrelation seems to be an important problem. Parameter estimates were derived using the maximum likelihood principle in the presence of first-order autocorrelation. Nonnested tests were used to select the appropriate formulation of the learning curve. Research conclusions are to use unit data when estimating a learning curve and to be prepared to treat autocorrelation if present.     

A two-phase robot selection procedure

In this paper, a two-phase methodology is proposed for robot selection. In phase 1, data envelopment analysis is used as a means to determine the technically efficient robot alternatives, considering cost and technical performance parameters. Using data envelopment analysis permits us to consider the fact that the performance parameters specified by the vendors are generally unattainable in practice. In the second phase, a fuzzy robot selection algorithm is utilized to rank the technically efficient robots according to both predetermined objective criteria and additional vendor-related subjective criteria. The algorithm is based on calculating fuzzy suitability indices for the technically efficient robot alternatives, and then, ranking the fuzzy indices to select the best robot alternative. A comprehensive example is provided to illustrate the decision procedure. The algorithm proposed in here is also applicable to a broader area of decision problems, e.g. facility site selection, determination of the best CNC machine or flexible manufacturing system among a set of mutually exclusive alternatives.     

Improved estimation of the stable laws

Fitting general stable laws to data by maximum likelihood is important but difficult. This is why much research has considered alternative procedures based on empirical characteristic functions. Two problems then are how many values of the characteristic function to select, and how to position them. We provide recommendations for both of these topics. We propose an arithmetic spacing of transform variables, coupled with a recommendation for the location of the variables. It is shown that arithmetic spacing, which is far simpler to implement, closely approximates optimum spacing. The new methods that result are compared in simulation studies with existing methods, including maximum-likelihood. The main conclusion is that arithmetic spacing of the values of the characteristic function, coupled with appropriately limiting the range for these values, improves the overall performance of the regression-type method of Koutrouvelis, which is the standard procedure for estimating general stable law parameters.     

Karlin's corollary: a topological approach to pitman's measure

In this article, many of the known univariate results about Pitman's Measure of Closeness (PMC) are synthesized through a topological approach. The proofs of many known results are simplified and clarified. The approach extends some previous results established under other restrictions. Connections between PMC and Bayesian estimation are discussed but the inherent interpretations differ. A discourse on this connection can be found in the article of Ghosh and Sen (1991). A transitiveness property for ordered estimators is established and a counter example is given for unordered ones. These results help distinguish between the Bayesian and classical interpretations of Pitman's measure.