Javaria Khan

The R package DELTD is for estimating densities by asymmetrical kernels and calculating MSE. This package is to estimate densities that are free of boundary bias. The major concern of the package is to enhance its usefulness in performing inference regarding stated kernels. For this purpose, some lifetime distributions, i.e. Beta, Birnbaum-Saunders, Erlang, Gamma, and Lognormal are considered here due to their usefulness in life data analysis, where their estimated values for density estimation can also be observed. Tuna data is also presented in this package. By using these kernels, densities will be free of boundary problems. This package is a collection of asymmetrical kernels which belong to the lifetime distribution.

Nonparametric regression is commonly used for summarizing the relationship between variables without requiring the assumptions of model. Generalized linear model and linear regression model are usually used to examine the relationship of variables, but both are badly affected by influential observations. Due to this, detection and removal of outliers attain a lot of attention of researchers to obtain reliable estimates. We focus on such robust technique whose performance is acceptable in the presence of outliers. The present article empirically compared the performance of linear regression model and generalized linear model with multivariate nonparametric kernel regression. Here, multivariate nonparametric kernel regression is used with Gaussian kernel and six different bandwidths on Aerial biomass data. The performance of nonparametric regression with Bayesian bandwidth was found to be better as compared with other methods.

The core objective of  current  study is to  investigate  the costs of financial distress of ongoing manufacturing sector of Pakistan. A panel of 146 manufacturing firms Pakistan are selected  for  this  study  for  the  period  of  2001-2011.  Two  most  applicable  panel  data techniques  (fixed effects and random effects models) are utilized to investigate  the costs of financial distress  and Hausman’s specification test recommended that fixed effects model is most appropriated model in this study. The results of fixed effects model suggest that financial  distress  of  on-going  firms  of  Pakistan  has  significant  direct  impact  on opportunity losses in case of Pakistan after control average collection period, total  assets growth,  fixed  to  total assets  ratio,  tangibility  of  assets  and  sector  distressed.  The upcoming studies must  explore direct costs of financial distress and bankruptcy in case of manufacturing as well as service sector of Pakistan.