• The Korean Journal of Applied Statistics
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• v.23 no.6
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• pp.1057-1065
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• 2010

In this paper we discuss some cautionary notes in using the Pearson chi-squared test statistic for the goodness-of-fit of the ordinal response model. If a model includes continuous type explanatory variables, the resulting table from the t of a model is not a regular one in the sense that the cell boundaries are not fixed but randomly determined by some other criteria. The chi-squared statistic from this kind of table does not have a limiting chi-square distribution in general and we need to be very cautious of the use of a chi-squared type goodness-of-t test. We also study the limiting distribution of the chi-squared type statistic for testing the goodness-of-t of cumulative logit models with ordinal responses. The regularity conditions necessary to the limiting distribution will be reformulated in the framework of the cumulative logit model by modifying those of Moore and Spruill (1975). Due to the complex limiting distribution, a parametric bootstrap testing procedure is a good alternative and we explained the suggested method through a practical example of an ordinal response dataset.

• Biomaterials and Biomechanics in Bioengineering
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• v.3 no.2
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• pp.85-104
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• 2016

Stability and loosening of implanted femoral stems in Total Hip Replacement have been well established as barriers to the primary concerns of osseointegration and long term implant survival. In-vitro experiments and finite element modeling have for years been used as a primary tool to assess the bone stem interface with variable methodologies leading to a wide range of micromotion, interference fit and stress shielding values in the literature. The current study aims to provide a comprehensive review of currently utilized methodologies for in-vitro mechanical testing as well as finite element modeling of both micromotion and interference of implanted femoral stems. A total of 12 studies detailed in 33 articles were selected for inclusion. Experimental values of micromotion ranged from 12 to $182{\mu}m$ while finite element analysis reported a wider range from 2.74 to $1,277{\mu}m$. Only two studies were found that modeled bone/implant contact with consideration for interference fit. In studies evaluating stem micromotion in THA, the reference surface at the bone/stem interface should be well defined. Additionally, the amount of penetration considered should be disclosed and associated with bone density and roughness.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.157-164
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• 2021

The aim of this study is to provide empirical evaluation of the accuracy of data-model fit using growing levels of invariance models. Overall model accuracy of factor solutions was evaluated by the examination of the order for testing three levels of measurement invariance (MIV) starting with configural invariance (model 0). Model testing was evaluated by the Chi-square difference test (∆𝛘²) between two groups, and root mean square error of approximation (RMSEA), comparative fit index (CFI), and Tucker-Lewis index (TLI) were used to evaluate the all-model fits. Factorial invariance result revealed that stability of the models was varying over increasing levels of measurement as a function of variable-to-factor ratio (VTF), subject-to-variable ratio (STV), and their interactions. There were invariant factor loadings and invariant intercepts among the groups indicating that measurement invariance was achieved. For VTF ratio (3:1, 6:1, and 9:1), the models started to show accuracy over levels of measurement when STV ratio was 6:1. Yet, the frequency of stability models over 1000 replications increased (from 69% to 89%) as STV ratio increased. The models showed more accuracy at or above 39:1 STV.

• Smart Structures and Systems
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• v.33 no.1
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• pp.65-91
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• 2024

The use of five optimization techniques for the prediction of a strength-based concrete mixture's best-fit model is examined in this work. Five optimization techniques are utilized for this purpose: Slime Mold Algorithm (SMA), Black Hole Algorithm (BHA), Multi-Verse Optimizer (MVO), Vortex Search (VS), and Whale Optimization Algorithm (WOA). MATLAB employs a hybrid learning strategy to train an artificial neural network that combines least square estimation with backpropagation. Thus, 72 samples are utilized as training datasets and 31 as testing datasets, totaling 103. The multi-layer perceptron (MLP) is used to analyze all data, and results are verified by comparison. For training datasets in the best-fit models of SMA-MLP, BHA-MLP, MVO-MLP, VS-MLP, and WOA-MLP, the statistical indices of coefficient of determination (R²) in training phase are 0.9603, 0.9679, 0.9827, 0.9841 and 0.9770, and in testing phase are 0.9567, 0.9552, 0.9594, 0.9888 and 0.9695 respectively. In addition, the best-fit structures for training for SMA, BHA, MVO, VS, and WOA (all combined with multilayer perceptron, MLP) are achieved when the term population size was modified to 450, 500, 250, 150, and 500, respectively. Among all the suggested options, VS could offer a stronger prediction network for training MLP.

• Communications for Statistical Applications and Methods
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• v.16 no.4
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• pp.697-705
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• 2009

The Pearson chi-squared statistic or the deviance statistic is widely used in assessing the goodness-of-fit of the generalized linear models. But these statistics are not proper in the situation of continuous explanatory variables which results in the sparseness of cell frequencies. We propose a goodness-of-fit test statistic for the cumulative logit models with ordinal responses. We consider the grouping of a dataset based on the ordinal scores obtained by fitting the assumed model. We propose the Pearson chi-squared type test statistic, which is obtained from the cross-classified table formed by the subgroups of ordinal scores and the response categories. Because the limiting distribution of the chi-squared type statistic is intractable we suggest the parametric bootstrap testing procedure to approximate the distribution of the proposed test statistic.

• Communications for Statistical Applications and Methods
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• v.26 no.2
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• pp.191-203
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• 2019

The problem of examining how well an assumed distribution fits the data of a sample is of significant and must be examined prior to any inferential process. The observed failure time data of items are often not wholly available in reliability and life-testing studies. Lowering the expense and period associated with tests is important in statistical tests with censored data. Goodness-of-fit tests for perfect data can no longer be used when the observed failure time data are progressive Type II censored (PC) data. Therefore, we propose goodness-of-fit test statistics and a graphical method based on generalized Lorenz curve for PC data from a location-scale distribution. The power of the proposed tests is then assessed through Monte Carlo simulations. Finally, we analyzed two real data set for illustrative purposes.

• Journal of the Korean Data and Information Science Society
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• v.5 no.2
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• pp.75-85
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• 1994

In this paper, I introduce the goodness-of-fit test statistics for exponential distribution using accelerated life test data. The ALT lifetime data were obtained by assuming step-stress ALT model, specially TRV model introduced by DeGroot and Goel(1979). The critical values are obtained for proposed test statistics, Kolmogorov-Smirnov, Kuiper, Watson, Cramer-von Mises, Anderson-Darling type, under various sample sizes and significance levels. The powers of the five test statistic are compared through Monte-Cairo simulation technique.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.197-212
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• 2002

When assessing goodness-of-fit of a model, a small subset of deviating observations can give rise to a significant lack of fit. It is therefore important to identify such observations and to assess their effects on various aspects of analysis. A Cook's distance measure is usually used to detect influential observation. But it sometimes is not fully effective in identifying truly influential set of observations because there may exist masking or swamping effects. In this paper we confine our attention to influential subset In GLMs such as logistic regression models and loglinear models. We modify a backwards stepping algorithm, which was originally suggested for detecting outlying cells in contingency tables, to detect influential observations in GLMs. The algorithm consists of two steps, the identification step and the testing step. In identification step we Identify influential observations based on influencial measures such as Cook's distances. On the other hand in testing step we test the subset of identified observations to be significant or not Finally we explain the proposed method through two types of dataset related to logistic regression model and loglinear model, respectively.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.24-27
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• 2005

The NDT(Non-Destructive Testing) is valid fur the defect detection of rolling stocks because it can be used to detect defects in invisible places. For example, in case of wheelsets fatigue cracks are initiated in the wheel seat that suffers from fretting fatigue damage. But the conventional ICFPD method can not be applied to detect such cracks in press-fit area of the axle by some technical problems. In this study, we introduced a new ICFPD (Induced Current Focusing Potential Drop) method that can be applied in press-fit area of the axle. And we performed the finite element analysis of the new ICFPD method using measured electromagnetic properties of the wheel and axle. It seems that our approach is very useful f3r the detection of defects in invisible places.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.483-497
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• 2001

When one fits a parametric density function to a data set, it is usually advisable to test the goodness of the postulated model. In this paper we study the nonparametric tests for testing the null hypothesis against general alternatives, when the null hypothesis specifies the density function up to unknown parameters. We modify the test statistic which was proposed by the first author and his colleagues. Asymptotic distribution of the modified statistic is derived and its performance is compared with some other tests through simulation.