A Perception Survey for the Evaluation of Urban Quality of Life in Kocaeli and a Comparison of the Life Satisfaction with the European Cities

Studies on urban quality of life (QoL) have been attracting lots of attention from various countries due to the deterioration of urban environment and decrease of the urban QoL. These studies that have been supported by international organizations such as United Nations, World Bank, OECD, European Commission and EUROSTAT (European Statistics) involve comparative assessment of life satisfaction in the European cities and comparing cities facilitate the exchange of experiment and improve the quality of local policies. The main objective of this study is to measure the local perceptions of QoL in Kocaeli, which is one of the important industrial cities of Turkey and compare the life satisfaction with the European cities. Generally, two different types of indicators have been used: objective and subjective indicators. The objective indicators cover five fields: socio-economic aspects, participation in civic life, education and training, environment and culture, and leisure. The subjective indicators are mainly for valuation of QoL perceptions in a city. In this research, a perception survey will be carried out to measure the local perceptions of QoL in Kocaeli. This survey will present on issues for which the residents in the Kocaeli had widely diverging opinions: employment opportunities, housing costs, safety, cleanliness of city, public transport, air quality and overall satisfaction with the QoL of their city. Thus, the study will become a major reference for local officials to improve QoL in Kocaeli and contribute to researches on QoL in cities.     

Adaptive Kalman Filtering with Multivariate Generalized Laplace System Noise

An adaptive Kalman filter is proposed to estimate the states of a system where the system noise is assumed to be a multivariate generalized Laplace random vector. In the presence of outliers in the system noise, it is shown that improved state estimates can be obtained by using an adaptive factor to estimate the dispersion matrix of the system noise term. For the implementation of the filter, an algorithm which includes both single and multiple adaptive factors is proposed. A Monte-Carlo investigation is also carried out to access the performance of the proposed filters in comparison with other robust filters. The results show that, in the sense of minimum mean squared state error, the proposed filter is superior to other filters when the magnitude of a system change is moderate or large.     

Survival functions for the frailty models based on the discrete compound Poisson process

Frailty models are often used to model heterogeneity in survival analysis. The distribution of the frailty is generally assumed to be continuous. In some circumstances, it is appropriate to consider discrete frailty distributions. Having zero frailty can be interpreted as being immune, and population heterogeneity may be analysed using discrete frailty models. In this paper, survival functions are derived for the frailty models based on the discrete compound Poisson process. Maximum likelihood estimation procedures for the parameters are studied. We examine the fit of the models to earthquake and the traffic accidents’ data sets from Turkey.     

A kalman filter in the presence of outliers

A Kalman Filtering algorithm which is robust to observational outliers is developed by assuming that the measurement error may come from either one of two Normal distributions, and that the transition between these distributions is governed by a Markov Chain. The resulting algorithm is very simple, and consists of two parallel Kalman Filters having different gains. The state estimate is obtained as a weighted average of the estimates from the two parallel filters, where the weights are the posterior probabilities that the current observation comes from either of the two distributions. The large improvements obtained by this Robust Kalman Filter in the presence of outliers is demonstrated with examples.     

Discrete-time survival analysis under ranked set sampling: an application to Turkish motor insurance data

Survival models with continuous-time data are still superior methods of survival analysis. However when the survival data is discrete, taking it as continuous leads the researchers to incorrect results and interpretations. The discrete-time survival model has some advantages in applications such as it can be used for non-proportional hazards, time-varying covariates and tied observations. However, it has a disadvantage about the reconstruction of the survival data and working with big data sets. Actuaries are often rely on complex and big data whereas they have to be quick and efficient for short period analysis. Using the mass always creates inefficient processes and consumes time. Therefore sampling design becomes more and more important in order to get reliable results. In this study, we take into account sampling methods in discrete-time survival model using a real data set on motor insurance. To see the efficiency of the proposed methodology we conducted a simulation study.