• 제목/요약/키워드: Statistical Model

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문맥의존 철자오류 후보 생성을 위한 통계적 언어모형 개선 (Improved Statistical Language Model for Context-sensitive Spelling Error Candidates)

  • 이정훈;김민호;권혁철
    • 한국멀티미디어학회논문지
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    • 제20권2호
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    • pp.371-381
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    • 2017
  • The performance of the statistical context-sensitive spelling error correction depends on the quality and quantity of the data for statistical language model. In general, the size and quality of data in a statistical language model are proportional. However, as the amount of data increases, the processing speed becomes slower and storage space also takes up a lot. We suggest the improved statistical language model to solve this problem. And we propose an effective spelling error candidate generation method based on a new statistical language model. The proposed statistical model and the correction method based on it improve the performance of the spelling error correction and processing speed.

통계(統計)/과학(科學) 데이타 베이스를 위한 개체(個體)-측면(側面) 모형(模型) (An Entity-Aspect Model for Statistical and Scientific Databases)

  • 유철중
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1987년도 전기.전자공학 학술대회 논문집(II)
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    • pp.1148-1152
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    • 1987
  • This paper analyzes the statistical and scientific entity-aspect model for statistical and scientific databases(SSDB's). The statistical and scientific entity-aspect model(SEAM) is defined an example of the application of the statistical and scientific entity-aspect model is represented. Finally, the statistical and scientific entity-aspect model as a design tool for SSDB is evaluated and the further research areas are suggested.

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Unbiasedness or Statistical Efficiency: Comparison between One-stage Tobit of MLE and Two-step Tobit of OLS

  • Park, Sun-Young
    • International Journal of Human Ecology
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    • 제4권2호
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    • pp.77-87
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    • 2003
  • This paper tried to construct statistical and econometric models on the basis of economic theory in order to discuss the issue of statistical efficiency and unbiasedness including the sample selection bias correcting problem. Comparative analytical tool were one stage Tobit of Maximum Likelihood estimation and Heckman's two-step Tobit of Ordinary Least Squares. The results showed that the adequacy of model for the analysis on demand and choice, we believe that there is no big difference in explanatory variables between the first selection model and the second linear probability model. Since the Lambda, the self- selectivity correction factor, in the Type II Tobit is not statistically significant, there is no self-selectivity in the Type II Tobit model, indicating that Type I Tobit model would give us better explanation in the demand for and choice which is less complicated statistical method rather than type II model.

Statistical Analysis of Transfer Function Models with Conditional Heteroscedasticity

  • Baek, J.S.;Sohn, K.T.;Hwang, S.Y.
    • Journal of the Korean Statistical Society
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    • 제31권2호
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    • pp.199-212
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    • 2002
  • This article introduces transfer function model (TFM) with conditional heteroscedasticity where ARCH concept is built into the traditional TFM of Box and Jenkins (1976). Model building strategies such as identification, estimation and diagnostics of the model are discussed and are illustrated via empirical study including simulated data and real data as well. Comparisons with the classical TFM are also made.

통계 정보를 이용한 전치사 최적 번역어 결정 모델 (A Statistical Model for Choosing the Best Translation of Prepositions.)

  • 심광섭
    • 한국언어정보학회지:언어와정보
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    • 제8권1호
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    • pp.101-116
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    • 2004
  • This paper proposes a statistical model for the translation of prepositions in English-Korean machine translation. In the proposed model, statistical information acquired from unlabeled Korean corpora is used to choose the best translation from several possible translations. Such information includes functional word-verb co-occurrence information, functional word-verb distance information, and noun-postposition co-occurrence information. The model was evaluated with 443 sentences, each of which has a prepositional phrase, and we attained 71.3% accuracy.

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사회적지지의 효과 모델 및 통계분석방법에 관한 국내간호논문 분석 (Major Effect Models of Social Support and Its Statistical Methods in Korean Nursing Research)

  • 이은현;김진선
    • 대한간호학회지
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    • 제30권6호
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    • pp.1503-1520
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    • 2000
  • The purpose of the present study is 1) to explain major effect models (main, moderating, and mediating) of social support and statistical methods for testing the effect models and 2) to analyze and evaluate the consistency in the use of the effect models and its statistical methods in Korean nursing studies. A total of 57 studies were selected from Journal of Korean Academy of Nursing, Journal of Korean Academic Society of Adult Nursing, Journal of Korean Women's Health Nursing Academic Society, Journal of Fundamentals of Nursing, Journal of Korean Community Nursing, Journal of Korean Psychiatric and Mental Health Nursing Academic Society, and Journal of Korean Pediatric Nursing Academic Society published in the year of 1990-1999. In results, most studies on social support performed in Korea Nursing Society were about a main effect model. There are few studies on moderating or mediating model of social support. Thus, it was difficult to find research findings how, why, under what conditions social support impacted on health outcomes. Most studies on the moderating or mediating effect model of social support used statistical methods for testing main effect model rather than for testing moderating or mediating effect model. That is, there are inconsistency between effect models of social support and its statistical methods in Korean nursing researches. Therefore, it is recommended to perform studies on moderating or mediating effect model and use appropriate statistical methods.

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Statistical Inference in Non-Identifiable and Singular Statistical Models

  • Amari, Shun-ichi;Amari, Shun-ichi;Tomoko Ozeki
    • Journal of the Korean Statistical Society
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    • 제30권2호
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    • pp.179-192
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    • 2001
  • When a statistical model has a hierarchical structure such as multilayer perceptrons in neural networks or Gaussian mixture density representation, the model includes distribution with unidentifiable parameters when the structure becomes redundant. Since the exact structure is unknown, we need to carry out statistical estimation or learning of parameters in such a model. From the geometrical point of view, distributions specified by unidentifiable parameters become a singular point in the parameter space. The problem has been remarked in many statistical models, and strange behaviors of the likelihood ratio statistics, when the null hypothesis is at a singular point, have been analyzed so far. The present paper studies asymptotic behaviors of the maximum likelihood estimator and the Bayesian predictive estimator, by using a simple cone model, and show that they are completely different from regular statistical models where the Cramer-Rao paradigm holds. At singularities, the Fisher information metric degenerates, implying that the cramer-Rao paradigm does no more hold, and that he classical model selection theory such as AIC and MDL cannot be applied. This paper is a first step to establish a new theory for analyzing the accuracy of estimation or learning at around singularities.

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파티클 필터기법을 통한 비선형 피로모델 개발 연구 (Development of Nonlinear Fatigue Model Based on Particle Filter Method)

  • 문성호
    • 한국도로학회논문집
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    • 제18권4호
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    • pp.63-68
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    • 2016
  • PURPOSES : The nonlinear model of fatigue cracking is typically used for determining the maintenance period. However, this requires that the model parameters be known. In this study, the particle filter (PF) method was used to determine various statistical parameters such as the mean and standard deviation values for the nonlinear model of fatigue cracking. METHODS : The PF method was used to determine various statistical parameters for the nonlinear model of fatigue cracking, such as the mean and standard deviation. RESULTS : On comparing the values obtained using the PF method and the least square (LS) method, it was found that PF method was suitable for determining the statistical parameters to be used in the nonlinear model of fatigue cracking. CONCLUSIONS : The values obtained using the PF method were as accurate as those obtained using the LS method. Furthermore, reliability design can be applied because the statistical parameters of mean and standard deviation can be obtained through the PF method.

고해상도 지상 기온 상세화 모델 개발 (Development of a High-Resolution Near-Surface Air Temperature Downscale Model)

  • 이두일;이상현;정형세;김연희
    • 대기
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    • 제31권5호
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    • pp.473-488
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    • 2021
  • A new physical/statistical diagnostic downscale model has been developed for use to improve near-surface air temperature forecasts. The model includes a series of physical and statistical correction methods that account for un-resolved topographic and land-use effects as well as statistical bias errors in a low-resolution atmospheric model. Operational temperature forecasts of the Local Data Assimilation and Prediction System (LDAPS) were downscaled at 100 m resolution for three months, which were used to validate the model's physical and statistical correction methods and to compare its performance with the forecasts of the Korea Meteorological Administration Post-processing (KMAP) system. The validation results showed positive impacts of the un-resolved topographic and urban effects (topographic height correction, valley cold air pool effect, mountain internal boundary layer formation effect, urban land-use effect) in complex terrain areas. In addition, the statistical bias correction of the LDAPS model were efficient in reducing forecast errors of the near-surface temperatures. The new high-resolution downscale model showed better agreement against Korean 584 meteorological monitoring stations than the KMAP, supporting the importance of the new physical and statistical correction methods. The new physical/statistical diagnostic downscale model can be a useful tool in improving near-surface temperature forecasts and diagnostics over complex terrain areas.