@article{
 title = {Selecting the Probability Distribution of Monthly Maximum Temperature of Dhaka (Capital City) in Bangladesh},
 type = {article},
 year = {2016},
 keywords = {Bangladesh,Model Selection,Probability Distribution,Temperature},
 pages = {33-45},
 volume = {33},
 id = {f99f0c77-3102-3ccc-bef3-5667892795c9},
 created = {2020-11-12T22:15:55.873Z},
 file_attached = {true},
 profile_id = {3d6b17c2-7de8-3a82-bf4f-ddb0e3081e5f},
 last_modified = {2021-05-05T17:42:11.788Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Rising temperature in the atmosphere causes sea level rise and affects low lying coastal areas and deltas of the world. The last decade of the twentieth century was globally the hottest since the beginning of worldwide temperature measurement during the nineteenth century. Many PDFs have been proposed in recent past, but in present study Weibull, Lognormal, Gamma, GEV, etc are used to describe the characteristics of maximum temperature. This paper attempts to determine the best fitted probability distribution of monthly maximum temperature. To identify the appropriate probability distribution of the observed data, this paper considers a data set on the monthly maximum temperature of Dhaka over the period January, 1972 to September, 2015. Goodness-of-fit criteria like the Kolmogrov-Smirnov (KS), the 2 R , the 2 χ and the Root Mean Squared Error (RMSE) were used in this paper to check the accuracy of the predicted data. According to the goodness-of-fit criteria and from the graphical comparisons it can be said that Generalized Skew Logistic distribution (GSL) provided the best fit for the observed monthly maximum temperature data of Dhaka station among the probability distribution considered in this paper.},
 bibtype = {article},
 author = {Hossian, Md Moyazzem and Abdulla, Faruq and Rahman, Md Habibur},
 journal = {Jahangirnagar University Journal of Statistical Studies}
}

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