Formulasi Model Kepemimpinan Selama Pandemi Covid-19 Pada Pimpinan Bank Mandiri Region Bandung Sebagai Upaya Menjaga Profitabilitas Perusahaan

This study aims to formulate a model of leadership in a strategic management framework referring to the leadership model literature in times of crisis with the results of deep interviews with top leaders in the region. This study uses a qualitative descriptive research approach where this research is a research method that utilizes qualitative data and is described descriptively. The type of research used is descriptive qualitative. The source of data in this study is primary data obtained by conducting questionnaire research in order to become the results and conclusions of the study. Using purposive sampling method. Samples were taken as many as 150 branches with the priority of the above criteria. In this study, it was found that the results of deep interviews and FGDs outline the strategy carried out during the pandemic is a survival strategy or profit strategy where companies seek to find sources of income that can still experience growth such as transactional digital transactions, e-commerce, investment transactions. and minimize the formation of costs arising from the decline in credit quality

Exploring dependence between categorical variables: benefits and limitations of using variable selection within Bayesian clustering in relation to log-linear modelling with interaction terms

This manuscript is concerned with relating two approaches that can be used to explore complex dependence structures between categorical variables, namely Bayesian partitioning of the covariate space incorporating a variable selection procedure that highlights the covariates that drive the clustering, and log-linear modelling with interaction terms. We derive theoretical results on this relation and discuss if they can be employed to assist log-linear model determination, demonstrating advantages and limitations with simulated and real data sets. The main advantage concerns sparse contingency tables. Inferences from clustering can potentially reduce the number of covariates considered and, subsequently, the number of competing log-linear models, making the exploration of the model space feasible. Variable selection within clustering can inform on marginal independence in general, thus allowing for a more efficient exploration of the log-linear model space. However, we show that the clustering structure is not informative on the existence of interactions in a consistent manner. This work is of interest to those who utilize log-linear models, as well as practitioners such as epidemiologists that use clustering models to reduce the dimensionality in the data and to reveal interesting patterns on how covariates combine.Comment: Preprin

Reversible jump methods for generalised linear models and generalised linear mixed models

A reversible jump algorithm for Bayesian model determination among generalised linear models, under relatively diffuse prior distributions for the model parameters, is proposed. Orthogonal projections of the current linear predictor are used so that knowledge from the current model parameters is used to make effective proposals. This idea is generalised to moves of a reversible jump algorithm for model determination among generalised linear mixed models. Therefore, this algorithm exploits the full flexibility available in the reversible jump method. The algorithm is demonstrated via two examples and compared to existing methods

Reversible jump methods for generalised linear models and generalised linear mixed models

A reversible jump algorithm for Bayesian model determination among generalised linear models, under relatively diffuse prior distributions for the model parameters, is proposed. Orthogonal projections of the current linear predictor are used so that knowledge from the current model parameters is used to make effective proposals. This idea is generalised to moves of a reversible jump algorithm for model determination among generalised linear mixed models. Therefore, this algorithm exploits the full flexibility available in the reversible jump method. The algorithm is demonstrated via two examples and compared to existing methods