137 research outputs found
On Some Bivariate Extensions of the Folded Normal and the Folded-T Distributions
No full textIn this paper two new bivariate distributions are defined and studied. They are two-variate versions of the folded normal distribution (Leone et al. 1961) and the folded t distribution (Psarakis and Panaretos 1990). They both arise in the context of evaluating the predictive behaviour of two competing linear models with the aim to select the one that leads to predictions closer to the actual value of the dependent variableFolded normal, Folded-t distribution, Model selection
On Some Bivariate Extensions of the Folded Normal and the Folded-T Distributions
No full textIn this paper two new bivariate distributions are defined and studied. They are two-variate versions of the folded normal distribution (Leone et al. 1961) and the folded t distribution (Psarakis and Panaretos 1990).
They both arise in the context of evaluating the predictive behaviour of two competing linear models with the aim to select the one that leads to predictions closer to the actual value of the dependent variabl
A Predictive Model Evaluation and Selection Approach - The Correlated Gamma Ratio Distribution
No full textIn this paper, an evaluation method is suggested for selecting one of two competing models based on certain predictive ability ratings. The main focus is on the case of linear models that are not necessarily nested. In the context of such models, the test procedure is based on a sample statistic whose distribution is shown to arise as the distribution of the ratio of two correlated gamma variables termed as the Correlated Gamma Ration Distribution. Percentage points of this distribution are obtained. The procedure is illustrated on real data.Model selection, Bivariate gamma distribution, F distribution, Correlated gamma-ratio distribution, Predictive ability
On a Distribution Arising in the Context of Comparative Model Performance Evaluation Problems
No full textThe paper deals with a distribution that arises as the distribution of a sample statistic used to compare the predictive ability of two competing linear models. It is defined as the distribution of the ratio of two correlated gamma variables and its probabilities are tabulated in order that they become readily available for practical useModel selection, Bivariate gamma distribution, F distribution
Multivariate Statistical Process Control Charts and the Problem of Interpretation: A Short Overview and Some Applications in Industry
No full textWoodall and Montgomery in a discussion paper, state that multivariate process control is one of the most rapidly developing sections of statistical process control. Nowadays, in industry, there are many situations in which the simultaneous monitoring or control, of two or more related quality - process characteristics is necessary. Process monitoring problems in which several related variables are of interest are collectively known as Multivariate Statistical Process Control (MSPC). This article has three parts. In the first part, we discuss in brief the basic procedures for the implementation of multivariate statistical process control via control charting. In the second part we present the most useful procedures for interpreting the out-of-control variable when a control charting procedure gives an out-of-control signal in a multivariate process. Finally, in the third, we present applications of multivariate statistical process control in the area of industrial process control, informatics, and businessQuality Control, Process Control, Multivariate Statistical Process Control, Hotelling's T², CUSUM, EWMA, PCA, PLS, Identification, Interpretation
Multivariate Statistical Process Control Charts: An Overview
No full textIn this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.quality control, process control, multivariate statistical process control, Hotelling's T-square, CUSUM, EWMA, PCA, PLS
On Certain Indices for Ordinal Data with Unequally Weighted Classes
No full textIn this paper, some new indices for ordinal data are introduced. These indices have been developed so as to measure the degree of concentration on the “small” or the “large” values of a variable whose level of measurement is ordinal. Their advantage in relation to other approaches is that they ascribe unequal weights to each class of values. Although, they constitute a useful tool in various fields of applications, the focus here is on their use in sample surveys and specifically in situations where one is interested in taking into account the “distance” of the responses from the “neutral” category in a given question. The properties of these indices are examined and methods for constructing confidence intervals for their actual values are discussed. The performance of these methods is evaluated through an extensive simulation study.
Graduating the age-specific fertility pattern using Support Vector Machines
No full textA topic of interest in demographic literature is the graduation of the age-specific fertility pattern. A standard graduation technique extensively used by demographers is to fit parametric models that accurately reproduce it. Non-parametric statistical methodology might be alternatively used for this graduation purpose. Support Vector Machines (SVM) is a non-parametric methodology that could be utilized for fertility graduation purposes. This paper evaluates the SVM techniques as tools for graduating fertility rates In that we apply these techniques to empirical age specific fertility rates from a variety of populations, time period, and cohorts. Additionally, for comparison reasons we also fit known parametric models to the same empirical data sets.age patterns of fertility, graduation techniques, parametric models of fertility, support vector machines
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