Drought is a complex natural hazard. Its several adverse impacts are prevailing in almost all climatic zones around the world. In this regards, drought monitoring and forecasting play a vital role in making drought mitigation policies. Therefore, several drought monitoring tools based on probabilistic models had been developed for precise and accurate inferences of drought severity and its effects. However, risk of inaccurate determination of drought classes always exists in probabilistic models. To overcome this issue, we proposed a new system based Probabilistic Weighted Joint Aggregative Drought Index (PWJADI) criterion for three multi-scalar drought indices, namely Standardized Precipitation Index (SPI), Standardized Precipitation Temperature Index (SPTI), and Standardized Precipitation Evapotranspiration Index (SPEI) at one-month time scale. By the basic assumption of the Markov chain, the PWJADI is based on the temporal switched weights that are propagated from the transition probability matrix of each temporal classification of drought index. Application of the proposed method is made for three meteorological stations of Pakistan. We found that our proposed model has ability to restructure the drought classes by capturing and bending the information from the historical behaviour of each drought class. Consequently, to make accurate and precise drought mitigation policies, the proposed method may integrate into effective drought monitoring systems.

Control charts are important statistical tool used to monitor fluctuations in the process location and dispersion parameters. The issues relating to the appropriate choice of control charts for the effective detection of process variability are addressed, and different control chart structures, such as Shewhart‐type, exponentially weighted moving average and cumulative sum are explored under ideal assumption of normality and contaminated normal environments, and hence, those control charts structures are identified which are more capable to detect aberrant changes in the process dispersion. Copyright © 2015 John Wiley & Sons, Ltd.

In this work, we provide a new generated class of models, namely, the extended generalized inverted Kumaraswamy generated (EGIKw-G) family of distributions. Several structural properties (survival function sf , hazard rate function hrf , reverse hazard rate function rhrf , quantile function qf and median, s th raw moment, generating function, mean deviation md , etc.) are provided. The estimates for parameters of new G class are derived via maximum likelihood estimation MLE method. The special models of the proposed class are discussed, and particular attention is given to one special model, the extended generalized inverted Kumaraswamy Burr XII (EGIKw-Burr XII) model. Estimators are evaluated via a Monte Carlo simulation MCS . The superiority of EGIKw-Burr XII model is proved using a lifetime data applications.

No abstract

No abstract