Journal of Data Science

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.

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.

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.

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

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.

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.

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 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.

Abstract: Existing indices of observer agreement for continuous data, such as the intraclass correlation coefficient or the concordance correlation coefficient, measure the total observer-related variability, which includes the variabilities between and within observers. This work introduces a new index that measures the interobserver variability, which is defined in terms of the distances among the ‘true values’ assigned by different observers on the same subject. The new coefficient of interobserver variability (CIV ) is defined as the ratio of the interobserver and the total observer variability. We show how to estimate the CIV and how to use bootstrap and ANOVAbased methods for inference. We also develop a coefficient of excess observer variability, which compares the total observer variability to the expected total observer variability when there are no differences among the observers. This coefficient is a simple function of the CIV . In addition, we show how the value of the CIV , estimated from an agreement study, can be used in the design of measurements studies. We illustrate the new concepts and methods by two examples, where (1) two radiologists used calcium scores to evaluate the severity of coronary artery arteriosclerosis, and (2) two methods were used to measure knee joint angle.