Overdispersion, often associated with count data is difficult to handle by a single
parameter regression model such as the Poisson regression model. Previous attempts to
modify the Poisson regression model with additional ...
Neural Network (NN) allows complex nonlinear relationships between the response
variables and its predictors. The Deep NN have made notable contributions across
computer vision, reinforcement learning, speech recognition ...
Generalised Multivariate Regression Estimators (GMREs) with multi-auxiliary
quantitative variables in multi-phase sampling have been used over time to estimate
the population mean. These estimators are structurally complex ...
The conventional Autoregressive Integrated Moving Average with Exogenous Variables
(arimax) model with Normal Error term and Multiple Linear Regression (MLR) require
stringent assumptions of normality of error term and ...
Regime change is the tendency of the Stock Market Returns (SMRs) for global market to change
their behaviour abruptly due to changes in financial regulations and policies. This behaviour has
no exemption to emerging ...
Unidimensional poverty analysis is the use of income or expenditure as an indicator for poverty. This approach relies heavily on the use of the Poverty Line (PL) as an arbitrary classification threshold to classify households ...
Benchmarking deals with problem of combining a series of high-frequency data with a series of low frequency data to form a single consistent time series. Various benchmarking methods in literature lack
some observations ...
Recent trends in poverty analysis have focused more on multidimensional poverty measures such as the Fuzzy Set Schemes (FSSs) rather than the unidimensional approaches. A major criticism of the unidimensional approaches ...
The Lee-Carter (LC) model was primarily designed for modelling the mortality pattern with Gaussian error structure in most developed countries. Attempts at using LC for describing the pattern of mortality in developing ...
Distributed Lag Model (DLM) is a major workhorse in dynamic single-equation regression, which
requires stringent assumptions for its validity. One of the critical assumptions of DLM is the
normality of the Error Term ...
Multicollinearity arises in econometrics when the regressor is linearly related to other regressors in a Normal Linear Regression Model (NLRM). A major drawback of the classical approach to the estimation of NLRM is that ...
Cure models are special survival models developed to estimate cure rate in cancer research. Several cure models such as Lognormal-Mixture Cure-Models (LNMCM), Loglogistics-Mixture-Cure-Models (LLMCM), Weibull-Mixture-Cure-Models ...
Most production functions are often nonlinear in parameters and difficult to linearise. The major shortcomings of the classical approach in estimating such parameters are varying complex inter- parametric relationship, ...
The estimation of static panel data model assumes homoscedastic error terms that is often violated in most economic models and when this happens, the Dynamic Panel Data Model (DPDM) is specified. The DPDM presumes correlation ...
Emergence of infectious diseases has renewed research interests in disease transmission modelling. Susceptible-Exposed-Infected-Recovered (S-E-I-R) model has been used for such studies. A major assumption of infectious ...
Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models have been used to model non-constant variances in financial time series models. Previous works have assumed error innovations of GARCH models of ...
The seasonal bilinear time series model is an effective tool in statistical analysis of seasonal time series data. The existing Seasonal Autoregressive Integrated Moving Average (SARIMA) bilinear model focused only on ...
Classical Regression Model (CRM) such as Weibull regression is commonly used for estimating
relationship among variables. The problem with CRM is its dependence on the
assumptions of normality and homoscedasticity of the ...