Home
Search

Journal of Data Science

Submit your article Information

Journal home
To appear
Current issue
All issues
More
Journal home To appear Current issue All issues

Detailed search

Title

Author

Types

Abstract

Keywords

Published

Pages

Volumes

Issues

DOI

Affiliation

Search results 892

Order by:

Select: All None Download:

On The Estimation Of The Shape Parameter Of A Symmetric Distribution

Jennifer S.K. Chan S.T. Boris Choy Stephen G. Walker

https://doi.org/10.6339/JDS.202001_18(1).0004

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 18, Issue 1 (2020), pp. 78–100

Abstract

The shape parameter of a symmetric probability distribution is often more difficult to estimate accurately than the location and scale parameters. In this paper, we suggest an intuitive but innovative matching quantile estimation method for this parameter. The proposed shape parameter estimate is obtained by setting its value to a level such that the central 1-1/n portion of the distribution will just cover all n observations, while the location and scale parameters are estimated using existing methods such as maximum likelihood (ML). This hybrid estimator is proved to be consistent and is illustrated by two distributions, namely Student-t and Exponential Power. Simulation studies show that the hybrid method provides reasonably accurate estimates. In the presence of extreme observations, this method provides thicker tails than the full ML method and protect inference on the location and scale parameters. This feature offered by the hybrid method is also demonstrated in the empirical study using two real data sets.

Determining an Optimum Biological Dose of A Metronomic Chemotherapy

Atanu Bhattacharjee Vijay M Patil

https://doi.org/10.6339/JDS.201701_15(1).0005

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 15, Issue 1 (2017), pp. 77–94

Abstract

The surrogate markers(SM) are the important factor for angiogenesis in cancer patients.In Metronomic Chemotherapy (MC) , physicians administer subtoxic doses of chemotherapy (without break) for long periods, to the target tumor angiogenesis. We propose a semiparametric approach, predictive risk modeling and time to control the level of surrogate marker to detect the perfect dose level of MC. It is based on the controlled level of surrogate marker, and the aim is to detect an Optimum Biological Dose (OBD) finding rather than a traditional Maximum Tolerated Dose (MTD) approach. The methods are illustrated with MC trial dataset to determine the best OBD and we investigate the performance of the model through simulation studies.

Geostatistical Analysis of Chinese Cancer Mortality: Variogram, Kriging and Beyond

Dejian Lai

https://doi.org/10.6339/JDS.2004.02(2).140

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 2, Issue 2 (2004), pp. 77–193

On the Application of Data Mining to Official Data

Hossein Hassani Shahin Gheitanchi Mohammad Reza Yeganegi

https://doi.org/10.6339/JDS.2010.08(1).578

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 8, Issue 1 (2010), pp. 75–89

Abstract

Abstract: Retrieving valuable knowledge and statistical patterns from official data has a great potential in supporting strategic policy making. Data Mining (DM) techniques are well-known for providing flexible and efficient analytical tools for data processing. In this paper, we provide an introduction to applications of DM to official statistics and flag the important issues and challenges. Considering recent advancements in software projects for DM, we propose intelligent data control system design and specifications as an example of DM application in official data processing.

A Note on Hypothesis Testing with Random Sample Sizes and its Relationship to Bayes Factors

Scott Berry Kert Viele

https://doi.org/10.6339/JDS.2008.06(1).380

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 6, Issue 1 (2008), pp. 75–87

Abstract

Abstract: Frequentist and Bayesian hypothesis testing are often viewed as “two separate worlds” by practitioners. While theoretical relationships of course exist, our goal here is to demonstrate a practical example where one must be careful conducting frequentist hypothesis testing, and in that context illustrate a practical equivalence between Bayesian and frequentist testing. In particular, if the sample size is random (hardly unusual in prac tical problems where the sample size may be “all available experimental units”), then choosing an α level in advance such as 0.05 and using it for every possible sample size is inadmissible. In other words, one can find a dif ferent overall procedure which has the same overall type I error but greater power. Not coincidentally, this alternative procedure is based on Bayesian testing procedures.

Interpretation of Epidemiological Data Using Multiple Correspondence Analysis and Log-linear Models

Demosthenes B. Panagiotakos Christos Pitsavos

https://doi.org/10.6339/JDS.2004.02(1).122

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 2, Issue 1 (2004), pp. 75–86

Abstract

Abstract: In this work we present a combined approach to contingency tables analysis using correspondence analysis and log-linear models. Several investigators have recognized relations between the aforementioned method ologies, in the past. By their combination we may obtain a better under standing of the structure of the data and a more favorable interpretation of the results. As an application we applied both methodologies to an epi demiological database (CARDIO2000) regarding coronary hert disease risk factors.a

Use of Generalized Maximum Entropy Estimation for Freight Flows Modelling and an Application

Esra Satici Haydar Demirhan

https://doi.org/10.6339/JDS.2012.10(1).1029

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 10, Issue 1 (2012), pp. 75–86

Abstract

Abstract: In this paper, freight transportation is taken into account. One of the models used for modelling “Origin-Destination” freight flows is log regression model obtained by applying a log-transformation to the tradi tional gravity model. Freight flows between ten provinces of Turkey is ana lyzed by using generalized maximum entropy estimator of the log-regression model for freight flow. The data set is gathered together from the axle load survey performed by Turkish Directorate of Highways and other so cioeconomic and demographic variables related with provinces of interest. Relations between considered socioeconomic and demographic variables and freight flows are figured out and results are discussed.

Usual Closed Birth Interval versus Most Recent Closed Birth Interval

Anup Kumar R. C. Yadava

https://doi.org/10.6339/JDS.201501_13(1).0005

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 13, Issue 1 (2015), pp. 73–94

Abstract

Abstract: : Normally, one may think that the distribution of closed birth interval of any specific order may be the same as the distribution of most recent closed birth interval of the same order. But it is not true. Here the distinction between the distribution of a specific order of usual closed birth interval and most recent closed birth interval of the same order is examined. In this context, firstly we demonstrate the distinction between the most recent closed birth interval and usual closed birth interval empirically by considering a real data set. Further, the distinction between these distributions is demonstrated theoretically, by taking certain hypothetical values of fertility parameters involved in the stochastic model proposed for the purpose.

Bayesian Semiparametric Sales Projections for the Texas Lottery

A. Majumdar R. Eubank

https://doi.org/10.6339/JDS.2009.07(1).435

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 7, Issue 1 (2009), pp. 73–87

Abstract

Abstract: State lotteries employ sales projections to determine appropri ate advertised jackpot levels for some of their games. This paper focuses on prediction of sales for the Lotto Texas game of the Texas Lottery. A novel prediction method is developed in this setting that utilizes functional data analysis concepts in conjunction with a Bayesian paradigm to produce predictions and associated precision assessments.

Efficiency Analysis of Manufacturing Firms Using Data Envelopment Analysis Technique

Tagdira Naznin Smriti Md Hasinur Rahaman Khan

https://doi.org/10.6339/JDS.201801_16(1).0004

Pub. online: 4 Aug 2022 Type: Research Article

Open Access

Journal: Journal of Data Science Volume 16, Issue 1 (2018), pp. 69–78

Abstract

Efficiency analysis is very useful and important to measure the performance of the firms in com- petitive market of rapidly developing country like Bangladesh. The more efficient firms, and the decision making units (DMUs) are usually referred as benchmarking units for the development. In this study, efficiency scores are obtained using the non-parametric Data Envelopment Anal- ysis (DEA) technique for 1007 manufacturing firms in Bangladesh from the enterprise survey data. The DEA is used to calculate weights for inputs and outputs by assigning the maximum efficiency score for a DMU under evaluation. Total 29 firms are found efficient under variable returns to scale assumption. The significant determinants behind the inefficiency found in this analysis include mainly the firm size, manager’s experience in respective sector, annual losses due to power outage, number of production workers.

72 73 74 75 76

Items per page

Export citation

Copy and paste formatted citation

Formatted citation

Placeholder

Citation style

Download citation in file

Export format

Authors

Placeholder

RSS

Journal of data science

Online ISSN: 1683-8602
Print ISSN: 1680-743X

About

About journal

For contributors

Submit
OA Policy
Become a Peer-reviewer

Contact us

JDS@ruc.edu.cn
No. 59 Zhongguancun Street, Haidian District Beijing, 100872, P.R. China