• Journal of the Korean Data and Information Science Society
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• v.22 no.1
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• pp.107-113
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• 2011

We present methods for studying the log-density ratio, which allow us to select which predictors are needed, and how they should be included in the logistic regression model. Under multivariate normal distributional assumptions, we investigate the form of the log-density ratio as a function of many predictors. The linear, quadratic and crossproduct terms are required in general. If two covariance matrices are equal, then the crossproduct and quadratic terms are not needed. If the variables are uncorrelated, we do not need the crossproduct terms, but we still need the linear and quadratic terms.

• The Korean Journal of Applied Statistics
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• v.26 no.1
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• pp.141-149
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• 2013

We present methods for studying the log-density ratio that enables the selection of the predictors and the form to be included in the logistic regression model. Under bivariate normal distributional assumptions, we investigate the form of the log-density ratio as a function of two predictors. If two covariance matrices are equal, then the crossproduct and quadratic terms are not needed. If the variables are uncorrelated, we do not need the crossproduct terms, but we still need the linear and quadratic terms. We also explore other conditions in which the crossproduct and quadratic terms are not needed in the logistic regression model.

• Communications for Statistical Applications and Methods
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• v.31 no.3
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• pp.349-363
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• 2024

Compositional data refers to data where the sum of the values of the components is a constant, hence the sample space is defined as a simplex making it impossible to apply statistical methods developed in the usual Euclidean vector space. A natural approach to overcome this restriction is to consider an appropriate transformation which moves the sample space onto the Euclidean space, and log-ratio typed transformations, such as the additive log-ratio (ALR), the centered log-ratio (CLR) and the isometric log-ratio (ILR) transformations, have been mostly conducted. However, in scenarios with sparsity, where certain components take on exact zero values, these log-ratio type transformations may not be effective. In this work, we mainly suggest an alternative transformation, that is the square-root transformation which moves the original sample space onto the directional space. We compare the square-root transformation with the log-ratio typed transformation by the simulation study and the real data example. In the real data example, we applied both types of transformations to the USG% data obtained from NBA, and used a density based clustering method, DBSCAN (density-based spatial clustering of applications with noise), to show the result.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.1171-1177
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• 2007

We consider estimation of the right-tail probability in a log-Laplace random variable, As we derive the density of ratio of two independent log-Laplace random variables, the k-th moment of the ratio is represented by a special mathematical function. and hence variance of the ratio can be represented by a psi-function.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.1-11
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• 2012

We present methods to study the log-density ratio of the conditional densities of the predictors given the response variable in the logistic regression model. This allows us to select which predictors are needed and how they should be included in the model. If the conditional distributions are skewed, the distributions can be considered as gamma distributions. A simulation study shows that the linear and log terms are required in general. If the conditional distributions of xjy for the two groups overlap significantly, we need both the linear and log terms; however, only the linear or log term is needed in the model if they are well separated.

• Journal of the Korean Data and Information Science Society
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• v.23 no.1
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• pp.99-111
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• 2012

The log-density ratio of the conditional densities of the predictors given the response variable provides useful information for variable selection in the logistic regression model. In this paper, we consider the predictors that are needed and how they should be included in the model. If the conditional distributions are skewed, the distributions can be considered as gamma distributions. Under this assumption, linear and log terms are generally included in the model. The log-odds graph is a very useful graphical tool in this study. A graphical study is presented which shows that if the conditional distributions of x|y for the two groups overlap significantly, we need both the linear and quadratic terms. On the contrary, if they are well separated, only the linear or log term is needed in the model.

• Journal of Plant Biology
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• v.15 no.2
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• pp.7-19
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• 1972

The studies of density effect or the effect of population density on plant growth have been done on basis of dry matter production with Raphanus acanthiformis var. simoodaeguen, Brassica campestris var. Pekinensis f. namsounsokoombecheu, Oryza sativa f. kimmajae and O. sativa f. mangyeng grown in the various spacing. 1. In the early period of plant growth in dry weight was not different each other among varying densities, but as time advanced the plant grown vast space grew sufficiently compared with those of narrow one. 2. Iogarithmic relation between the growth of plant (W) and the density (P), log W-log P in the material plants, were approximated by two straight lines, one was horizontal line and another inclined: the former showed non-competition density and the latter competition density addition to these the point interlinking both lines were implied of the optimum density per unit land area at certain growth period. 3. The values of relatvie growth rate (RGR) and net assimilation rate (NAR) were decreased as increase in the density, while those of leaf area ratio (LAR) were rather increased in the same condition, with minor exception. From these results and relation between the productive structure and due to lack of the recieved light intensity owing to the mutal shading among the plants.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.3C
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• pp.216-225
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• 2007

This paper presents bit splitting methods to apply multilevel modulation to iterative codes such as turbo code, low density parity check code and turbo product code. Log-likelihood ratio method splits multilevel symbols to bits using the received in-phase and quadrature component based on Gaussian approximation. However it is too complicate to calculate and implement hardware due to exponential and log calculation. therefore this paper presents Euclidean, MAX and Sector method to reduce the high complexity of LLR method. We propose optimal bit splitting method for three iterative codes.

• Communications for Statistical Applications and Methods
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• v.2 no.1
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• pp.112-121
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• 1995

Let $p_1$ and $p_2$ be the proportions of two populations. To test the hypothesis $H_0 : p_1 = p_2$, we usually use the $x^2$ statistic, the large sample binomial statistic Z, and the Generalized Likelihood Ratio statistic-2log $\lambda$developed based on different mathematical rationale, respectively. Since testing the above hypothesis is equivalent to testing whether two populations follow the common Bernoulli distribution, one may also test the hypothesis by comparing 1 with the ratio of each density estimate and the hypothesized common density estimate, called comparison density, which was devised by Parzen(1988). We show that the above traditional test statistics ate actually estimating the measure of distance between the true densities and the common density under $H_0$ by representing them with the comparison density.

• Journal of the Korean GEO-environmental Society
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• v.14 no.3
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• pp.13-17
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• 2013

Determination of geotechnical characteristics of soil is either to use the field samples to measure the characteristics of soil through laboratory test or measuring the characteristics directly in the field. Field test can be derived similar value by considering characteristics of site and laboratory test can be confirmed the characteristic of soil by testing with field samples. This article describes relative density as the measure of compaction for cohesionless soils and presents several simple and mathematical relationships to help engineers estimate needed parameters for relative density calculations. The main purpose of this research is to investigate possible correlations between coefficient of uniformity, coefficient of curvature, maximum and minimum void ratio, mean grain size. Results show a linear relationship between the minimum and maximum void ratios and a power function relationship between coefficient of uniformity and the limiting void ratios. Void ratio range, which is the difference between the maximum and minimum void ratios, appeared to be log normally distributed but showed no simple mathematical fit to the data. these results were shown to help engineers estimate needed parameters for relative density calculations.