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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. 相似文献
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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. 相似文献
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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. 相似文献
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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. 相似文献
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Nihal Ata Tutkun Nursel Koyuncu Uğur Karabey 《Journal of Statistical Computation and Simulation》2019,89(4):660-667
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. 相似文献
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