A new parametric regression model is developed based on the gamma-Maxwell distribution. Monte Carlo simulations show the accuracy of the maximum likelihood estimators. The proposed model explains COVID-19 mortality rates of the 50 U.S. largest cities.

Seasonality is an inherent part of most of the epidemic data. The fixed coefficient INAR(1) models with seasonal structure have been studied by many authors. The varying immunity and susceptibility affect the chances of catching or escaping an infection. This brings in the randomness in the phenomenon of the spread of the diseases. The fixed coefficient INAR models assume that the chance of infection remains the same for every individual, which is not true practically and hence one needs to study the disease spread phenomenon using random coefficient INAR models. The parameters of the proposed model have been estimated using quasi maximum likelihood estimation. Various probabilistic and inferential properties of the model have been studied. A simulation study has been carried out for parameter estimation. Two data sets having seasonal structures have been analyzed using the model. The model fits well to the data sets compared to the existing models.

Conjoint Analysis is an analytical technique used in market research to quantify consumer preferences for a product or service. The result of the technique provides us with different combinations of attributes that consumers value the most. The technique uses different features of a product or service and their corresponding levels to model and quantifies the customers’ buying decision process. Using Conjoint Analysis, one can study the trade-offs undertaken by the consumer and use the derived insights for design implementation. The sale of Bluetooth Earphones in India witnessed a sudden spike during the COVID 19 pandemic. The consequential market, thriving with competition, saw market leaders attempt to strengthen their positions further and new players try to enter the market. The paper seeks to understand the existing consumer preferences for different features of Bluetooth Earphones using Conjoint Analysis. The outcome of the analysis provides market stakeholders with a yardstick to guide their design decisions and formulate sustainable long-term business strategies.

This study attempts to explore the influence of observations in a time series or a discrete time signal. The goal is to detect abnormal observations from a frequency domain point of view, while the most of relevant studies have been done from a time domain point of view. The concept of the influence function in the field of robust statistics is borrowed to identify influential observations in a time series. An empirical version of the influence function on the discrete Fourier transform of a time series is designed and subsequently a statistic is proposed to identify influential observations of a time series from the frequency domain point of view. Though the proposed statistic is simple enough to be calculated with simple arithmetic operations, case studies show that the proposed method is capable of identifying influential or abnormal observations of a time series. By identifying influential or abnormal observations, we would be able to gain a better understanding of the nature of a time series and to control possible future influential observations.

The 2nd Sustainable Development Goal (SDG) of the United Nations attempt to eliminate the potential hunger and food insecurity issues by 2030, but in the plight of COVID19 pandemic it has become far more critical and persistent issue globally as well as in India. The nation-wide socio-economic surveys of National Sample Survey Office (NSSO) in India are designed to produce reliable and representative estimates of important food insecurity parameters at state and national level for both rural and urban sectors separately but these surveys cannot be used directly to generate reliable district level estimates. Whereas, efficient and representative disaggregated level estimates for the extent (or incidence) of food insecurity prevalence has direct impact on strategizing effective policy plans and monitoring progress towards eliminating food insecurity. In this backdrop, the paper outlines small area estimation approach to estimate the incidence of food insecurity across the districts of rural Uttar Pradesh in India by linking data from the 2011–12 Household Consumer Expenditure Survey of NSSO, and the 2011 Indian Population Census. A spatial map has been generated showing spatial disparity for the incidence of food insecurity in Uttar Pradesh. These disaggregated level estimates are relevant and purposeful for SDG indicator 2.1.2 – severity of food insecurity. The estimates and map of food insecurity incidences are expected to deliver invaluable information to the policy-analysts and decision-makers.

The Auto Regressive Integrated Moving Average (ARIMA) model seems not to easily capture the nonlinear patterns exhibited by the 2019 novel coronavirus (COVID-19) in terms of daily confirmed cases. As a result, Artificial Neural Network (ANN) and Error, Trend, and Seasonality (ETS) modeling have been successfully applied to resolve problems with nonlinear estimation. Our research suggests that it would be ideal to use a single model of ETS or ARIMA for COVID-19 time series forecasting rather than a complicated Hybrid model that combines several models. We compare the forecasting performance of these models using real, worldwide, daily COVID-19 data for the period between January 22, 2020 till June 19, and June 20 till January 2, 2021 which marks two stages, each stage indicating the first and the second wave respectively. We discuss various forecasting approaches and the criteria for choosing the best forecasting technique. The best forecasting model selected was compared using the forecasting assessment criterion known as Mean Absolute Error (MAE). The empirical results show that the ETS and ARIMA models outperform the ANN and Hybrid models. The main finding from the ETS and ARIMA models analysis indicate that the magnitude of the increase in total confirmed cases over time is declining and the percentage change in the death rate is also on the decline. Our results shows that the chosen forecaste models are consistent during the first and second wave of of the pandemic. These forecasts are encouraging as the world struggles to contain the spread of COVID-19. This may be the result of the social distancing measures mandated by governments worldwide.

Human race is under the COVID-19 pandemic menace since beginning of the year 2020. Even though the disease is easily transmissible, a massive fraction of the affected people is recovering. Most of the recovered patients will not experience death due to COVID-19, even if they observed for a long period. They can be treated as long term survivors in the context of lifetime data analysis. In this article, we present statistical methods to estimate the proportion of long term survivors (cure fraction) of the COVID-19 patient population in India. The proportional hazards mixture cure model is used to estimate the cure fraction and the effect of the covariates gender and age, on lifetime. We can see that the cure fraction of the COVID-19 patients in India is more than 90%, which is indeed an optimistic information.

Traditionally, asymptotic tests are studied and applied under local alternative (Aivazian, et al., 1985). There exists a widespread opinion that the Wald, likelihood ratio, and score tests are asymptotically equivalent. We dispel this myth by showing that These tests have different statistical power in the presence of nuisance parameters. The local properties of the tests are described in terms of the first and second derivative evaluated at the null hypothesis. The comparison of the tests are illustrated with two popular regression models: linear regression with random predictor and logistic regression with binary covariate. We study the aberrant behavior of the tests when the distance between the null and alternative does not vanish with the sample size. We demonstrate that these tests have different asymptotic power. In particular, the score test is generally asymptotically biased but slightly superior for linear regression in a close neighborhood of the null. The power approximations are confirmed through simulations.

BACKGROUND: Spirometric prediction equations are used to evaluate lung function in the clinical setting. However, such equations are not yet available for First Nations populations. The purpose of this study is to derive appropriate spirometric reference equations for a group of Cree First Nations school-aged children and adolescents living in rural Saskatchewan, Canada. METHODS: Spirometric data was collected from Cree First Nations cohort living in rural Saskatchewan, Canada. In the baseline survey, 351 children and adolescents participated and of these 134 were identified as healthy non-smoking individuals. The predicted values and Lower Limit of Normal (LLN) of spirometric indices were calculated for school-going children and adolescents (ages 6–17 years for males and 6–14 years for females). The spirometric indices were assumed to follow a Box-Cox-Cole-Green (BCCG) distribution with median, μ, coefficient of variation, σL and skewness, ν. Akaike Information Criteria (AIC) approach was used to obtain the reference models. The LLN was calculated by taking the fifth percentile of the prediction equations of the lung function variables. RESULTS: Two-sample t-test revealed significant differences in the means of lung function and demographic measurements between both boys and girls. Therefore, separate equations were fitted for both boys and girls. CONCLUSION: This study provides the baseline lung function reference equations for Cree First Nations children and adolescents. This baseline study provides a platform for future studies, which can be conducted to improve the accuracy of the predicted lung function indices for such study cohort.