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When facing small numbers of observations or rare events, political scientists often encounter separation, in which explanatory variables perfectly predict binary events or nonevents. In this situation, maximum likelihood provides implausible estimates and the researcher might want incorporate some form of prior information into the model. The most sophisticated research uses Jeffreys' invariant prior to stabilize the estimates. While Jeffreys' prior has the advantage of being automatic, I show that it often provides too much prior information, producing smaller point estimates and narrower confidence intervals than even highly skeptical priors. To help researchers assess the amount of information injected by the prior distribution, I introduce the concept of a partial prior distribution and develop the tools required to compute the partial prior distribution of quantities of interest, estimate the subsequent model, and summarize the results.

Computer simulation is one of several technologies available to improve competitiveness, and simulation is thus often used as a design and/or decision tool in various industries including supply chain systems. The presumed difficult and time‐consuming statistical analysis of simulation data is often avoided while doing simulation studies by supplying deterministic input data to simulation models. This article addresses this issue in order to make managers aware of the risks involved with this practice. Embracing any technology that is new to the organisation requires responsibility. A theoretical comparison of deterministic simulation versus stochastic simulation is conducted and the theoretical results are substantiated with empirical results obtained from a simple logistic simulation model using deterministic input as one alternative and stochastic input as a second alternative.

In this paper, the binary classification problem of multi‑dimensional functional data is considered. To solve this problem a regression technique based on functional logistic regression model is used. This model is re‑expressed as a particular logistic regression model by using the basis expansions of functional coefficients and explanatory variables. Based on re‑expressed model, a classification rule is proposed. To handle with outlying observations, robust methods of estimation of unknown parameters are also considered. Numerical experiments suggest that the proposed methods may behave satisfactory in practice.

Oppenheim et al. (2015) provides the first empirical analysis of insurgent defection during armed rebellion, estimating a series of multinomial logit models of continued rebel participation using a survey of ex-combatants in Colombia. Unfortunately, many of the main results from this analysis are an artifact of separation in these data – that is, one or more of the covariates perfectly predicts the outcome. We demonstrate that this can be identified using simple cross tabulations. Furthermore, we show that Oppenheim et al.'s (2015) results are not supported when separation is explicitly accounted for. Using a generalization of Firth's (1993) penalized-likelihood estimator – a well-known solution for separation – we are unable to reproduce any of their conditional results. While our (re-)analysis focuses on Oppenheim et al. (2015), this problem appears in other research using multinomial logit models as well. We believe that this is both because the discussion on separation in political science has primarily focused on binary-outcome models, and because software (Stata and R) does not warn researchers about seperation in multinomial logit models. Therefore, we encourage researchers using multinomial logit models to be especially vigilant about separation, and discuss simple red flags to consider.

Some useful simplification of the theory of political mobilization is achieved by treating the ideology of a mobilizing agent as an innovation. The process of political mobilization is then treated as a process of innovation diffusion. This perspective allows the development of the necessary linkage between the micro- & macro-level components of the process. The theory is tested using extended time series for seven societies -- the US, UK, Sweden, Nazi Germany, Fascist Italy, the USSR, & the People's Republic of China -- & is supported. 8 Tables, 7 Figures, 1 Appendix, 16 References. HA.

SIMPLIFICATION OF THE THEORY OF POLITICAL MOBILIZATION IS ACHIEVED BY TREATING THE IDEOLOGY OF A MOBILIZING AGENT AS AN INNOVATION. THE PROCESS OF POLITICAL MOBILIZATION IS THEM TREATED AS A PROCESS OF INNOVATION OF NECESSARY LINKA BETWEEN THE MICRO AND MACRO LEVEL COMPONENTS OF THE PROCESS. EXTENDED TIME SERIES FOR SEVEN SOCIETIES UTILIZED.