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This study is an empirical analysis of sovereign credit ratings (SCR) in South Africa (SA) using Logistic Regression (LR) to identify their determinants and forecast SCRs. Data of macroeconomic indicators including SCRs from 1999 to 2020 in quarterly format were classified and analyzed to identify indicators utilized by Credit Rating Agencies (CRAs) and then predict future ratings CRAs take various information from political, infrastructure, financial, economic, regional, local, and other factors pertaining to a country and assess the ability of that country to pay its debt. This information is then presented through a grading scale termed rating, with the highest rating country being highly creditworthy and lowest rating likely to default. There are three major CRAs, namely, Fitch, Moodys and Standard and Poors. The study identified the use of different macroeconomic indicators by CRAs as well as different techniques in assessing and assigning sovereign credit ratings. The study points out that Household Debt to Disposable Income Ratio (HDDIR) was the most influential variable on SCRs. HDDIR, exchange rates and the inflation rate were the most crucial variables for guessing credit ratings. Policymakers should aim to reduce household debt in relation to disposable income, implement policies that strengthen the local currency and stabilize as well as lower inflation. Investors should watch out on nations that have high household debt levels as this may spill over into credit risk.

AbstractEach year, more than $3 billion is wagered on the NCAA Division 1 men's basketball tournament. Most of that money is wagered in pools where the object is to correctly predict winners of each game, with emphasis on the last four teams remaining (the Final Four). In this paper, we present a combined logistic regression/Markov chain model for predicting the outcome of NCAA tournament games given only basic input data. Over the past 6 years, our model has been significantly more successful than the other common methods such as tournament seedings, the AP and ESPN/USA Today polls, the RPI, and the Sagarin and Massey ratings. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006.

"Defining and Operationalizing Context Through a Structural Political Geography for International Relations" published on by Oxford University Press.

This article describes a model for representing functional competencies in students with disabilities. Although strategies exist to identify skills and activities that are functional for students with severe handicaps, these strategies provide relatively little information on how to separate the functional skills into meaningful component parts that represent the range of behaviors needed in the natural environment. Data are presented to illustrate the narrow range of behaviors included in task analyses in current literature on skill acquisition. The Component Model of Functional Life Routines provides a systematic alternate approach to delineating the behaviors required in natural environments.

Background Security concerns have been raised since big data became a prominent tool in data analysis. For instance, many machine learning algorithms aim to generate prediction models using training data which contain sensitive information about individuals. Cryptography community is considering secure computation as a solution for privacy protection. In particular, practical requirements have triggered research on the efficiency of cryptographic primitives. Methods This paper presents a method to train a logistic regression model without information leakage. We apply the homomorphic encryption scheme of Cheon et al. (ASIACRYPT 2017) for an efficient arithmetic over real numbers, and devise a new encoding method to reduce storage of encrypted database. In addition, we adapt Nesterov's accelerated gradient method to reduce the number of iterations as well as the computational cost while maintaining the quality of an output classifier. Results Our method shows a state-of-the-art performance of homomorphic encryption system in a real-world application. The submission based on this work was selected as the best solution of Track 3 at iDASH privacy and security competition 2017. For example, it took about six minutes to obtain a logistic regression model given the dataset consisting of 1579 samples, each of which has 18 features with a binary outcome variable. Conclusions We present a practical solution for outsourcing analysis tools such as logistic regression analysis while preserving the data confidentiality. ; This work was partly supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.B0717-16-0098) and by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIP) (No.2017R1A5A1015626). MK was supported in part by NIH grants U01TR002062 and U01EB023685. Publication of this article has been funded by the NRF Grant funded by the Korean Government (MSIT) (No.2017R1A5A1015626).

In this paper a review will be given of some methods available for modelling relationships between categorical response variables and explanatory variables. These methods are all classed under the name polytomous logistic regression (PLR). Models for PLR will be presented and compared; model parameters will be tested and estimated by weighted least squares and by likelihood. Usually, software is needed for computation, and available statistical software is reported.An industrial problem is solved to some extent as an example to illustrate the use of PLR. The paper is concluded by a discussion on the various PLR‐methods and some topics that need a further study are mentioned.

In August 2003, the Ghanaian Government made history by implementing the first National Health Insurance System (NHIS) in Sub-Saharan Africa. Within three years, over half of the countrys population had voluntarily enrolled into the National Health Insurance Scheme. This study had three objectives: 1) To estimate the risk factors that influences the Ghana national health insurance claims. 2) To estimate the magnitude of each of the risk factors in relation to the Ghana national health insurance claims. In this work, data was collected from the policyholders of the Ghana National Health Insurance Scheme with the help of the National Health Insurance database and the patients attendance register of the Koforidua Regional Hospital, from 1st January to 31st December 2011. Quantitative analysis was done using the generalized linear regression (GLR) models. The results indicate that risk factors such as sex, age, marital status, distance and length of stay at the hospital were important predictors of health insurance claims. However, it was found that the risk factors; health status, billed charges and income level are not good predictors of national health insurance claim. The outcome of the study shows that sex, age, marital status, distance and length of stay at the hospital are statistically significant in the determination of the Ghana National health insurance premiums since they considerably influence claims. We recommended, among other things that, the National Health Insurance Authority should facilitate the institutionalization of the collection of appropriate data on a continuous basis to help in the determination of future premiums.

The study focused on the one-parameter logistic model (1PLM) and its' application in test development. Two research questions were answered and the design of the study was the instrumentation. Instrumentation research is a scientific investigation for meticulous development or construction of a test or measuring instrument that validity measures that concept or psychological construct, which it intends to measure with all accuracy (Kpolovie, 2010). A multi-stage sampling technique was used to acquire a sample size of 200 students for the study. A simple random sampling technique was used to select four schools from the local government area. Stratified random sampling was used to select 50 students each from each of the four schools to give the needed sample of 200 for the study. The instrument for this study was a self-developed mathematics test items. It is a 50 items test. The format is multiple-choice objectives with five (5) options lettered A-E. Findings revealed that the use of one parameter latent trait theory (Rasch model) offers the opportunity to deal with core measurement issues such as construct validity as well as providing richer interpretation regarding examinee performance.