The changing professional organization: A review of competing archetypes

The aim of this paper is to summarize literature relevant to the professional organization and to present a contemporary analysis of the archetype concept in this field. In order to understand recent and ongoing changes in professional organizations, the paper begins with a review of how the professional archetype evolved from the 1960s to 1990. Then, with examples from contemporary accounting, health care and law organizations, it considers the processes by which an institutionalized archetype can change. Forces for change – such as deregulation, competition, technology and globalization – can challenge the interpretive scheme and eventually delegitimize the existing archetype. At the same time, significant environmental changes can override isomorphic pressures and de‐institutionalize the long‐accepted structures. Thus we herald the emergence a new professional archetype – or perhaps several competing archetypes. Finally, the paper reviews the evolving field of professional organization as a whole, and understands the extant archetypes. A typology of professional organizations is proposed that currently seems to have three clusters of organizations – possibly three competing archetypes – namely, the traditional professional partnership, the specialized ‘Star’ form and the corporate global professional network, or GPN.     

通过谈判争取成功砝码

回想你上次面临新的领导机遇时,出现了什么情况？不管你是受命领导团队、部门还是整个企业,你很可能通过谈判争取该职位的薪水和待遇,比如：头衔、假期及奖金,但你是否争取过在该职位上取得成功的条件呢?     

Pipage Rounding: A New Method of Constructing Algorithms with Proven Performance Guarantee

The paper presents a general method of designing constant-factor approximation algorithms for some discrete optimization problems with assignment-type constraints. The core of the method is a simple deterministic procedure of rounding of linear relaxations (referred to as pipage rounding). With the help of the method we design approximation algorithms with better performance guarantees for some well-known problems including MAXIMUM COVERAGE, MAX CUT with given sizes of parts and some of their generalizations.     

A Joint Modeling Approach for Longitudinal Outcomes and Non-ignorable Dropout under Population Heterogeneity in Mental Health Studies

The paper proposes a joint mixture model to model non-ignorable drop-out in longitudinal cohort studies of mental health outcomes. The model combines a (non)-linear growth curve model for the time-dependent outcomes and a discrete-time survival model for the drop-out with random effects shared by the two sub-models. The mixture part of the model takes into account population heterogeneity by accounting for latent subgroups of the shared effects that may lead to different patterns for the growth and the drop-out tendency. A simulation study shows that the joint mixture model provides greater precision in estimating the average slope and covariance matrix of random effects. We illustrate its benefits with data from a longitudinal cohort study that characterizes depression symptoms over time yet is hindered by non-trivial participant drop-out.KEYWORDS: Latent growth curve, MNAR drop-out, survival analysis, finite mixture model, mental health     

Regression models using the LINEX loss to predict lower bounds for the number of points for approximating planar contour shapes

Researchers in statistical shape analysis often analyze outlines of objects. Even though these contours are infinite-dimensional in theory, they must be discretized in practice. When discretizing, it is important to reduce the number of sampling points considerably to reduce computational costs, but to not use too few points so as to result in too much approximation error. Unfortunately, determining the minimum number of points needed to achieve sufficiently approximate the contours is computationally expensive. In this paper, we fit regression models to predict these lower bounds using characteristics of the contours that are computationally cheap as predictor variables. However, least squares regression is inadequate for this task because it treats overestimation and underestimation equally, but underestimation of lower bounds is far more serious. Instead, to fit the models, we use the LINEX loss function, which allows us to penalize underestimation at an exponential rate while penalizing overestimation only linearly. We present a novel approach to select the shape parameter of the loss function and tools for analyzing how well the model fits the data. Through validation methods, we show that the LINEX models work well for reducing the underestimation for the lower bounds.     

Estimation of the parameters of symmetric stable ARMA and ARMA–GARCH models

In this article, we first propose the modified Hannan–Rissanen Method for estimating the parameters of autoregressive moving average (ARMA) process with symmetric stable noise and symmetric stable generalized autoregressive conditional heteroskedastic (GARCH) noise. Next, we propose the modified empirical characteristic function method for the estimation of GARCH parameters with symmetric stable noise. Further, we show the efficiency, accuracy and simplicity of our methods with Monte-Carlo simulation. Finally, we apply our proposed methods to model the financial data.     

蒙古尔部族的组织

本文介绍了蒙古尔部族的组成、形成和发展,并对其族长、土司的特点和承继作了详尽的描述。本文介绍了蒙古尔部族的组成、形成和发展,并对其族长、土司的特点和承继作了详尽的描述。     

蒙古尔部族的组织(续二)

本文对蒙古尔部族的组成、形成和发展作了介绍,其中对族长、土司的特点和承继作了详尽的描述。     

Effects of child support and welfare policies on nonmarital teenage childbearing and motherhood

This paper is an assessment of the impact of child support enforcement and welfare policies on nonmarital teenage childbearing and motherhood. We derive four hypotheses about the effects of policies on nonmarital teenage childbearing and motherhood. We propose that teenage motherhood and school enrollment are joint decisions for teenage girls. Based on individual trajectories during ages 12–19, our analysis uses an event history model for nonmarital teenage childbearing and a dynamic model of motherhood that is jointly determined with school enrollment. We find some evidence that child support policies indirectly reduce teen motherhood by increasing the probability of school enrollment, which, in turn, reduces the probability of teen motherhood. This finding suggests that welfare offices may wish to place greater weight on outreach programs that inform more teenagers of the existence of strong child support enforcement measures. Such programs might reduce nonmarital teen motherhood further and thus reduce the need for welfare support and child support enforcement in the long run.

A new heteroscedastic regression to analyze mass loss of wood in civil construction in Brazil

A heteroscedastic regression based on the odd log-logistic Marshall–Olkin normal (OLLMON) distribution is defined by extending previous models. Some structural properties of this distribution are presented. The estimation of the parameters is addressed by maximum likelihood. For different parameter settings, sample sizes and some scenarios, various simulations investigate the performance of the heteroscedastic OLLMON regression. We use residual analysis to detect influential observations and to check the model assumptions. The new regression explains the mass loss of different wood species in civil construction in Brazil.