KeywordsHidden markov model Estimators Time series Algorithm States Sequence Probability.The Hidden Markov Model (HMM) is a powerful statistical tool for modeling generative sequences that can be characterized by an underlying process generating an observable sequence. Hidden Markov Model is one of the most basic and extensively used statistical tools for modeling the discrete time series. In this paper using transition probabilities and emission probabilities different algorithm are computed and modeled the series and the algorithms to solve the problems related to the hidden markov model are presented. Hidden markov models face some problems like learning about the model, evaluation process and estimate of parameters included in the model. The solution to these problems as forward-backward, Viterbi, and Baum Welch algorithm are discussed respectively and also useful for computation. A new hidden markov model is developed and estimates its parameters and also discussed the state space model.
The magnificent seven statistical process control (SPC) tools are commonly used to monitor the variation in the process. Control charts are the most efficient and real‐time monitoring tool from the SPC toolkit. A control chart visually differentiates the effect of special or inherent cause variations and indicates the process as out‐of‐control when such a special cause exists. For single occasion problems, simple random sampling (SRS) is used as a sampling design. When the quality of a product is assessed at regular intervals, then modified successive sampling (MSS) is chosen as the preferred sampling mechanism. Hence, this paper intends to propose EWMA‐MSS(S) and CUSUM‐MSS(S) charts under MSS schemes to monitor moderate to small shifts in the location parameter of a process. We presented the performance evaluation of EWMA‐MSS(S) and CUSUM‐MSS(S) charts in terms of run length metrics and compared them with the Shewhart‐MSS(S) control chart. The outcomes show that the EWMA‐MSS(S) perform amazingly well as compared to the Shewhart‐MSS(S) and CUSUM‐MSS(S) control charts. Further, illustration of the proposed charts on the QSAR aquatic toxicity dataset is also used to demonstrate the application of the proposed charts.
All investors are very keen to know about the trend of the Contribution/ OriginalityThis study contributes in the existing literature related to forecasting of daily gold price. In this study, a methodology of statistical time series modelling is utilized known as Box-Jenkins. It is found that, model formulated by this methodology perform better than the other models presented in literature.
Migration is the most common reason of the rapid urbanization. A rapid migration occurs due to many causes like SocioEconomic , Cultural, Demographic and Psychological factors. This study is designed in context of these factors to determine the reasons behind the migration. The aim of this study is to light up the migration behavior of people in changing patterns of Demographic and SocioEconomic structure. We conduct the present study about the district Sargodha. Our study evaluates the urbanization behavior of this district i.e. Sargodha. Our study area is Sargodha. For the selection of respondents, purposive area was selected. Our sample size is about 380 and technique used in analysis was simple random sampling and the respondent are both male and female of Sargodha. With the help of designed questionnaire, we collected data. Our questionnaires having questions related to all SocioEconomic variables like Education, Literacy, Health Facilities and Unemployment etc. Furthermore, questions describe the purpose of migration and inspiration factors (pull & push factors) and asked to confined their opinion and its consequence. Then we analyse the data through suitable statistical techniques. This study reveals that, 30% people move towards cities for higher education, 29.2% people for business opportunities and 26.8% for employment. We conclude on the basis of our results that work, education, employment and business opportunities are the main factors to influence urbanization in Sargodha. The push factors are major cause of urbanization as compare to pull factors. Results also shows that majority of the respondents are adult and educated but not trained professional.
In this era of Industry 4.0, efficient and affordable monitoring solutions are needed for the surveillance of manufacturing/service operations. In general, memory-type control charts outperform memoryless control charts when it comes to determining the changes in location and dispersion parameters of symmetrically distributed processes. Before monitoring the process location, it is essential to monitor the process dispersion, since the latter presumes that the process variance remains stable. In practice, the modified successive sampling (MSS) mechanism is preferred over simple random sampling for its cost-effectiveness and efficiency. This study was designed in order to propose moving average and double moving average control charts based on the MSS mechanism for monitoring the dispersion parameter. The performance of the proposed charts is evaluated using run-length measures, and a comparison is made with an existing control chart based on MSS and repetitive sampling. Furthermore, the application of the designed moving and double moving average charts is demonstrated using a case study related to fertilizer production. It is observed that the proposed double moving average control chart performs better than the other control charts designed under the MSS and repetitive sampling schemes.
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