Variance estimation for tree-order restricted models

In this article, we discuss the estimation of the common variance of several normal populations with tree-order restricted means. We discuss the asymptotic properties of the maximum-likelihood estimator (MLE) of the variance as the number of populations tends to infinity. We consider several cases of various orders of the sample sizes and show that the MLE of the variance may or may not be consistent or be asymptotically normal.     

Borrowing strength from past data in small domain prediction by kalman filtering - a case

Point and interval estimators for small domains based exclusively on current and domain specific sample observations are generally ineffective because of inadequate sample-sizes. So, borrowing strength from sample values for analogous domains and simultaneously from all relevant past and auxiliary data is useful in deriving improved small domain statistics. Postulating for simplicity a linear regression model with a single covariate and a zero intercept but a time-specific domain-invariant slope we start with “synthetic” generalized regression predictors for the domain totals. These borrow across only domains. For further improvements a simple autoregressive model is postulated for the slope parameters. Employing Kalman filtering the previous predictors are revised to borrow supplementary strength across time. As drastic simplifying assumptions are needed in such predictions the efficacy of the procedure is examined through an empirical exercise using live data as well as simulations. The numerical findings turn out encouraging.     

A finite population quantile estimation by unequal probability sampling

Abstract

Using a model-assisted approach, this paper studies asymptotically design-unbiased (ADU) estimation of a population “distribution function” and extends to deriving an asymptotic and approximate unbiased estimator for a population quantile from a sample chosen with varying probabilities. The respective asymptotic standard errors and confidence intervals are then worked out. Numerical findings based on an actual data support the theory with efficient results.     

On the bias and mean-square error of order-restricted maximum likelihood estimators

The inverse Gaussian family (IG) (μ,λ) is a versatile family for modelling nonnegative right-skewed data. In this paper, we propose robust methods for testing homogeneity of the scale-like parameters λ_i from k independent IG populations subject to order restrictions. Robustness of the procedures is examined for a variety of IG-symmetric alternatives including lognormal and the recently introduced contaminated inverse Gaussian populations. Our study shows that these inference procedures for the inverse Gaussian scale-like parameters and their properties exhibit striking similarities to those of the scale parameters of the normal distribution.     

The Many Faces of a Neutral Face: Head Tilt and Perception of Dominance and Emotion

Based on the premise that human head tilt is homologous to animal dominance displays, we hypothesized that when a head is bowed, the face should be perceived as submissive, sad, displaying inferiority emotions (i.e., shame, embarrassment, guilt, humiliation, and respect) and, paradoxically, as contracting the zygomatic major muscle. Conversely, a raised head should be perceived as more dominant and displaying greater superiority emotions (i.e., contempt and pride). We conducted two experiments showing 3-D models of faces to 64 participants. The results confirmed our hypotheses and also showed that a raised head connotes happiness. In addition, we found a significant influence of the actors' sex on participants' perception, such as a bias towards perceiving stronger upward contraction of the mouth in female than male actors when the head is tilted. We discuss these findings within the context of evolution and social behavior.     

A New Perspective on Violent Crime Burden Index: Evidence from Indian Districts

Given limited resource availability in a developing nation like India, faced with high incidences of crime, it is important to optimize on the resources spent in combating crime by channelling them to proper direction. This requires an understanding of the actual and overall level of crime across India. Our paper provides a complete understanding of the various indicators of violent crime and the determinants of these crimes in India using district level data for three census years, namely, 1981, 1991 and 2001. We construct three alternative crime-burden indices. Including a variable like voter turnout in state election at the district level, we document significant impact of public awareness to reduce and combat crime. The constructed crime burden index shows that states located in northern parts of India have more incidences of crime compared to states in the south. We also find that our estimated crime-burden indices tend to report in general a higher level of crime-burden than the average based index. This suggests controlling for the factors beyond population while constructing the aggregate crime-burden index for any country is essential. Our work although is limited to the Indian data, we however, believe that this can be easily applied to various other countries.     

Generalised Linear Models Incorporating Population Level Information: An Empirical Likelihood Based Approach

In many situations information from a sample of individuals can be supplemented by population level information on the relationship between a dependent variable and explanatory variables. Inclusion of the population level information can reduce bias and increase the efficiency of the parameter estimates.Population level information can be incorporated via constraints on functions of the model parameters. In general the constraints are nonlinear making the task of maximum likelihood estimation harder. In this paper we develop an alternative approach exploiting the notion of an empirical likelihood. It is shown that within the framework of generalised linear models, the population level information corresponds to linear constraints, which are comparatively easy to handle. We provide a two-step algorithm that produces parameter estimates using only unconstrained estimation. We also provide computable expressions for the standard errors. We give an application to demographic hazard modelling by combining panel survey data with birth registration data to estimate annual birth probabilities by parity.     

Production lot sizing with process deterioration and machine breakdown under inspection schedule

The paper develops integrated production, inventory and maintenance models for a deteriorating production system in which the production facility may not only shift from an ‘in-control’ state to an ‘out-of-control’ state but also may break down at any random point in time during a production run. In case of machine breakdown, production of the interrupted lot is aborted and a new production lot is started when the on-hand inventory is depleted after corrective repair. The process is inspected during each production run to examine the state of the production process. If it is found in the ‘in-control’ state then either (a) no action is taken except at the time of last inspection where preventive maintenance is done (inspection policy-I) or (b) preventive maintenance is performed (inspection policy-II). If, however, the process is found to be in the ‘out-of-control’ state at any inspection then restoration is done. The proposed models are formulated under general shift, breakdown and repair time distributions. As it is, in general, difficult to find the optimal production policy under inspection policy-I, a suboptimal production policy is derived. Numerical examples are taken to determine numerically the optimal/suboptimal production policies of the proposed models, to examine the sensitivity of important model parameters and to compare the performance of inspection and no inspection policies.     

Market Entry Costs,Producer Heterogeneity,and Export Dynamics

As the exchange rate, foreign demand, and production costs evolve, domestic producers are continually faced with two choices: whether to be an exporter and, if so, how much to export. We develop a dynamic structural model of export supply that characterizes these two decisions. The model embodies plant‐level heterogeneity in export profits, uncertainty about the determinants of future profits, and market entry costs for new exporters. Using a Bayesian Monte Carlo Markov chain estimator, we fit this model to plant‐level panel data on three Colombian manufacturing industries. We obtain profit function and sunk entry cost coefficients, and use them to simulate export responses to shifts in the exchange‐rate process and several types of export subsidies. In each case, the aggregate export response depends on entry costs, expectations about the exchange rate process, prior exporting experience, and producer heterogeneity. Export revenue subsidies are far more effective at stimulating exports than policies that subsidize entry costs.