Keya Rani Das
In statistics it is conventional to assume that the observations are normal. The entire statistical framework is grounded on this assumption and if this assumption is violated the inference breaks down. For this reason it is essential to check or test this assumption before any statistical analysis ...
Shahidul Islam Khan, Abu Sayed Md. Latiful Hoque
Abstract In data analytics, missing data is a factor that degrades performance. Incorrect imputation of missing values could lead to a wrong prediction. In this era of big data, when a massive volume of data is generated in every second, and utilization of these data is a major concern to the stakeh...
Adrian Grant, D. G. Altman, Abdel Babiker, Marion Campbell et al.
OBJECTIVES: To address issues about data monitoring committees (DMCs) for randomised controlled trials (RCTs). DATA SOURCES: Electronic databases. Handsearching of selected books. Personal contacts with experts in the field. REVIEW METHODS: Systematic literature reviews of DMCs and small group proce...
Md. Mizanur Rahman, Mosab I. Tabash, Aidin Salamzadeh, Selajdin Abduli et al.
Abstract Collecting data using an appropriate sampling technique is a challenging task for a researcher to do. The researchers will be unable to collect data from all possible situations, which will preclude them from answering the study’s research questions in their current form. In light of the en...
Nasrin Khatun
In this study, to power comparison test, different univariate normality testing procedures are compared by using new algorithm. Different univariate and multivariate test are also analyzed here. And also review efficient algorithm for calculating the size corrected power of the test which can be use...
Md. Kamrul Hasan, Md. Ashraful Alam, Shidhartho Roy, Aishwariya Dutta et al.
Recently, numerous studies have been conducted on Missing Value Imputation (MVI), intending the primary solution scheme for the datasets containing one or more missing attribute’s values. The incorporation of MVI reinforces the Machine Learning (ML) models’ performance and necessitates a systematic ...
M. Ataharul Islam, Rafiqul I. Chowdhury, K Singh
The covariate dependence in a higher order Markov models is examined. First order Markov models with covariate dependence are discussed and are generalized for higher order. A simple alternative is also proposed. The estimation procedure is discussed for higher order with a number of covariates. The...
B. M. Golam Kibria, Shipra Banik
The estimation of ridge parameter is an important problem in the ridge regression method, which is widely used to solve multicollinearity problem. A comprehensive study on 28 different available estimators and five proposed ridge estimators, KB1, KB2, KB3, KB4, and KB5, is provided. A simulation stu...
Mohammad Shafiqur Rahman, Mahbuba Sultana
BACKGROUND: When developing risk models for binary data with small or sparse data sets, the standard maximum likelihood estimation (MLE) based logistic regression faces several problems including biased or infinite estimate of the regression coefficient and frequent convergence failure of the likeli...
Jahar B. Choudhury
Non-parametric maximum likelihood estimation of the cause specific failure probability, and of its standard error, in the presence of competing risks is discussed with reference to some contraceptive use dynamics data from Bangladesh. The cause specific incidence function provides an intuitively app...
Bo‐Cheng Wei, Yue‐Qing Hu, Wing–Kam Fung
The generalized leverage of an estimator is defined in regression models as a measure of the importance of individual observations. We derive a simple but powerful result, developing an explicit expression for leverage in a general M ‐estimation problem, of which the maximum likelihood problems are ...
A. T. M. Shakil Ahamed, Navid Tanzeem Mahmood, Md. Nazmul Hossain, Mohammad Tanzir Kabir et al.
Agricultural crop production depends on various factors such as biology, climate, economy and geography. Several factors have different impacts on agriculture, which can be quantified using appropriate statistical methodologies. Applying such methodologies and techniques on historical yield of crops...
Md. Jamal Uddin, Rolf H. H. Groenwold, M. Sanni Ali, Anthonius de Boer et al.
Background Unmeasured confounding is one of the principal problems in pharmacoepidemiologic studies. Several methods have been proposed to detect or control for unmeasured confounding either at the study design phase or the data analysis phase. Aim of the Review To provide an overview of commonly us...
Md Hasinur Rahaman Khan, Ewart Shaw
In public health, demography and sociology, large-scale surveys often follow a hierarchical data structure as the surveys are based on multistage stratified cluster sampling. The appropriate approach to analyzing such survey data is therefore based on nested sources of variability which come from di...
Bangladesh. Parisaṃkhyāna Bibhāga