In this example, we reanalyze the soybean yield data, but treat the explanatory variable as a categorical (ordinal) variable with 4 levels and use one−way anova to test for a significant SO2 effect. We can think of the data as samples from four different populations, corresponding to the SO2 concentration the chambers received, with possibly different means. In each treatment group we have 3 ob...

This appendix discusses numerous robustness checks of our baseline estimates of the effect of financial distress on output. All of the alternatives described use our new measure of financial distress as the explanatory variable. We discuss the results using both industrial production and real GDP as the output variable. However, for simplicity, the graphs only show the results for real GDP. Eac...

In many area of medical research, a relation analysis between one response variable and some explanatory variables is desirable. Regression is the most common tool in this situation. If we have some assumptions for such normality for response variable, we could use it. In this paper we propose a nonparametric regression that does not have normality assumption for response variable and we focus ...

identification of a real time of a change in a process, when an out-of-control signal is present is significant. this may reduce costs of defective products as well as the time of exploring and fixing the cause of defects. another popular topic in the statistical process control (spc) is profile monitoring, where knowing the distribution of one or more quality characteristics may not be appropr...

This paper studies estimation of multiplicative, unobserved components panel data models without imposing the strict exogeneity assumption on the explanatory variables. The method of moments estimators proposed have significant robustness properties; they require only a conditional mean assumption, and apply to models with lagged dependent variables, finite distributed lag models that allow arb...

The weighted likelihood approach is used to perform robust inference on the parameters in a generalized linear models. We distinguish the case of replicated observations of the dependent variable for each combination of the explanatory variables, common in the design of experiment framework, and the case of one observation for each combination of the explanatory variables, very common in observ...

This paper compares three approaches for solving the problem of measurement error in a hierarchical gain score model. The pre-test and post-test scores are IRT scores with measurement error. Explanatory variables at student level and class level are considered in the model. Simulation results show that the gain score model that does not consider measurement error overestimates the explanatory v...

Integer valued AR (INAR) processes are perfectly suited for modelling count data. We consider the inclusion of explanatory variables into the INAR model to extend the applicability of INAR models and give an alternative to Poisson regression models. An efficient MCMC algorithm is constructed to analyze the model and incorporates both explanatory variable and order selection. The methodology is ...

Beta is found to be a function of several leading economic indicators and government policy variables within the context of the Variable Beta Model which incorporates economic characteristics in the single index model in a multiplicative manner. When contemporaneous macroeconomic descriptors are replaced with reporting-period-lagged macroeconomic descriptors, in the Lagged Variable Beta Model, ...