• Communications for Statistical Applications and Methods
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• v.11 no.2
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• pp.297-311
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• 2004

In the sample survey for a highly skewed population, stratum jumpers often occur. Stratum jumpers are units having large discrepancies between a stratification variable and a study variable. We propose two models for stratum jumpers: a multiplicative model and a random replacement model. We also consider the modification of the L-H stratification algorithm such that we apply the previous models to L-H algorithm in determination of the sample sizes and the stratum boundaries. We evaluate the performances of the new stratification algorithms using real data. The result shows that L-H algorithm for the random replacement model outperforms other algorithms since the estimator has the least coefficient of variation.

• Journal of the Korean Statistical Society
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• v.14 no.2
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• pp.100-117
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• 1985

This paper describes an outline of the sampling design for crop area survey and product survey in Korea. The design attempts to from a double statification, to obtain an efficient allocation of the sample and to reduce the sampling error by establishign crop concentrated strata. The optimum numbers of sample field and sample plot are investigated. The design is made it possible to reduce the sampling errors as well as to reduce the sample size further than the present survey.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.201-204
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• 1997

This study is focussed on optimization problems which require allocating the restricted inventory to demand points and assignment of vehicles to routes in order to deliver goods for demand sites with optimal decision. This study investigated an integrated model using three step-by-step approach based on relationship that exists between the inventory allocation and vehicle routing with restricted amount of inventory and transportations. we developed several sub-models such as; first, an inventory-allocation model, second a vehicle-routing model based on clustering and a heuristic algorithms, and last a vehicle routing scheduling model, a TSP-solver, based on genetic algorithm. Also, for each sub-models we have developed computer programs and by a sample run it was known that the proposed model to be a very acceptable model for the inventory-allocation and vehicle routing problems.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.144-152
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• 2009

Construction contracts involve the allocation or distribution of the risks inherent to a construction project between or among contracting parties. However, it has been a common practice that only one party drafts the contract due to practical reasons and particular policies of various organizations. Interviews were conducted on some local contractors to gain their meaningful insights and standpoints on the allocation of each risk. These results were compared with the actual risk allocation using the Philippine government standard contract and risk principles from the literature to determine if their considered opinions provide a plausible alternative. A sample application of this evaluation is presented for construction-related risks and risk allocation recommendations are provided in the end.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1155-1167
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• 2009

We made a new sample design for life and consciousness survey of Kyungpook people in 2007. We used the 10% sample survey data of 2005 population and housing census as a survey population. After stratification, we allocate proportionally samples within strata after examining various characteristics in previous survey, which includes economic activity state, an income level per year, and housing possession. And we calculated weight in a new sample design and derived estimators and a formula of standard error using the weights.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• The Korean Journal of Applied Statistics
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• v.18 no.1
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• pp.27-41
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• 2005

We made a new sample design for intestinal parasitic infection survey in 2004. We used the 10% sample survey data of 2000 population and housing census as a survey population. Since the infection rates of intestinal parasitics are very low, we applied the relative risk and odds ratio instead of ordinary method such as t-test to study the characteristics from the 1997 survey data. In order to allocate samples to stratum, we used the compromise of Neyman allocation which is the average of three Neyman allocations. And also, we derive estimators and variance estimators of the estimators.

• Journal of the Korean Home Economics Association
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• v.32 no.2
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• pp.49-60
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• 1994

Recently there has been new work schedule developed in some companies which means starting the work at 7 and finishing at 4 This article studied on the effects of new work schedule on the time allocation by married couples. It was compared with the time spent by married couples in group of old work schedule. And it studied whether the change of work schedule affects the time allocation and satisfaction of couples. This survey was conducted from November to December 1993 One hundred and twenty none couples were considered to be valid sample for this study. The findings of this study are as follows: 1. Couples with new work schedule did perceive changes in the time allocation. 2. The total time for transportation and market work of husbands was decreased and leisure time showed a increase when there is new work schedule. 3. Husbands with new work schedule perceived that they spent more time in the household work leisure sharing with their children and sharing with spouses than husbands with old work schedule. 4. Couples with new work schedule showed higher marital satisfaction and family life satisfaction than couples with old work schedule.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1113-1120
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• 2015

We propose a multi-proportions randomized response model by stratified simple random sampling for surveys of sensitive issues of a polychotomous population composed of several stratum. We also systemize a theoretical validity to apply multi-proportions randomized response model (Abul-Ela et al.' model, Eriksson's model) to stratified simple random sampling and derive the estimate and its dispersion matrix of the proportion of sensitive characteristic of population using the suggested model. Two types of sample allocations (proportional allocation and optimum allocation) are considered under the fixed cost. In efficiency, the Eriksson's model by stratified sampling are compared to the Abul-Ela et al.' model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.95-104
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• 2017

This paper deals with solution methods for discrete and multi-valued optimization problems. The objective function of the problem incorporates noise effects generated in case that fitness evaluation is accomplished by computer based experiments such as Monte Carlo simulation or discrete event simulation. Meta heuristics including Genetic Algorithm (GA) and Discrete Particle Swarm Optimization (DPSO) can be used to solve these simulation based multi-valued optimization problems. In applying these population based meta heuristics to simulation based optimization problem, samples size to estimate the expected fitness value of a solution and population (particle) size in a generation (step) should be carefully determined to obtain reliable solutions. Under realistic environment with restriction on available computation time, there exists trade-off between these values. In this paper, the effects of sample and population sizes are analyzed under well-known multi-modal and multi-dimensional test functions with randomly generated noise effects. From the experimental results, it is shown that the performance of DPSO is superior to that of GA. While appropriate determination of population sizes is more important than sample size in GA, appropriate determination of sample size is more important than particle size in DPSO. Especially in DPSO, the solution quality under increasing sample sizes with steps is inferior to constant or decreasing sample sizes with steps. Furthermore, the performance of DPSO is improved when OCBA (Optimal Computing Budget Allocation) is incorporated in selecting the best particle in each step. In applying OCBA in DPSO, smaller value of incremental sample size is preferred to obtain better solutions.