Capturing asymmetry in COVID-19 counts using an improved skewness measure for time series data.

Capturing asymmetry among time series is an important area of research as it provides a range of information regarding the behaviour and distribution of the underlying series, which in turn proves to be useful for prediction. Classically, this can be achieved by modeling the skewness of the underlying series, usually using the standard measure. We present here an improved measure of skewness for time series which are integrated by a certain order, which is easy to calculate and proves to be advantageous over the existing one.

Impact of COVID-19 lockdowns on house sparrows: Comparative study from an Indian context.

Due to the outbreak of COVID-19, the last couple of years have been drastic in terms of human behavioural patterns. The pandemic has taught us key lessons about crisis, communication and misinformation. People were forced to stay at home for a very long duration because of the strict lockdown measures imposed by governments all over the globe.

Sequentially estimating the dynamic contact angle of sessile saliva droplets in view of SARS-CoV-2.

Estimating the contact angle of a virus infected saliva droplet is seen to be an important area of research as it presents an idea about the drying time of the respective droplet and in turn of the growth of the underlying pandemic. In this paper we extend the data presented by Balusamy, Banerjee and Sahu ["Lifetime of sessile saliva droplets in the context of SARS-CoV-2," Int. J.

Maximum precision estimation for a step-stress model using two-stage methodologies.

In this paper, we consider a two-stage sequential estimation procedure to estimate the parameters of a cumulative exposure model under an accelerated testing scenario. In particular, we focus on a step-stress model where the stress level changes after a pre-specified number of failures occur, which is also random. This is termed as a 'random stress change time' in the literature.

Middle censoring in the multinomial distribution with applications.

In a multinomial set-up with k possible outcomes, we develop estimation under a "middle censoring" paradigm, which is as defined in Jammalamadaka and Mangalam (2003). This problem has many special features because of the inter-dependent probabilities, which we explore here.