NONPARAMETRIC AUTOCOVARIANCE FUNCTION ESTIMATION

Nonparametric estimators of autocovariance functions for non-stationary time series are developed. The estimators are based on straightforward nonparametric mean function estimation ideas and allow use of any linear smoother (e.g. smoothing spline, local polynomial). The paper studies the properties of the estimators, and illustrates their usefulness through application to some meteorological and seismic time series.     

Returning to psychotherapy with the same therapist: A self psychological perspective

Terminations or interruptions of psychotherapy which have been followed by a patient's resuming therapy with the same therapist at a later time have often been viewed as premature with the implication that the initial therapy has been inadequate or deficient. This paper suggests, however, that a patient returns to the same therapist because the selfobject bond established in the initial treatment is still intact, and that the hiatus between treatments can be a time of further development and strengthening of self structure. Two clinical cases are described which illustrate these ideas.     

On the dynamics of populations with two age groups

By restricting the number of age groups to two and by introducing continued fractions, the dynamics of populations with deterministically changing vital rates are completely specified at all times. Explicit expressions for the sequence of the product of population projection matrices are given. From these, relative age distribution and reproductive values to any desired accuracy are obtained as functions of the vital rates. Thus, the results given in this paper are stronger than any existing weak ergodic theorems.     

Prediction in Some Poisson Errors in Variables Models

Predictive distributions are developed and illustrated for prediction in some Poisson errors in variables models. Two different situations in which multiplicative treatment effects are appropriate are considered within the context of predicting counts of road accidents. Hierarchical prior structures are investigated, and numerical integration and Gibbs sampling routines are used to derive the predictive and posterior probabilities. Examples of analyses are provided with data from road accidents in Sweden.     

Experiencing Work: Values, Attitudes, and Moods

This paper describes a model (the VAM model)which integrates three of the most significantdimensions of the work experience, work values,attitudes, and moods, into one overarching framework.The VAM model proposes that a rich and more completeunderstanding of the experience of work necessitates thesimultaneous consideration of work values, attitudes,and moods. After describing how work values, attitudes, and moods, each capture key aspects ofexperiencing work, we discuss three important dimensionsalong which they vary: time, dynamism, and focus. Thesedimensions underscore the need for the simultaneous consideration of values, attitudes, and moodsfor a fuller understanding of the work experience. Wethen describe how work values, attitudes, and moods havethe potential to influence each other. Finally, we discuss the implications of the VAM modelfor understanding important organizational outcomesincluding extra-role behavior, job performance, socialloafing, absenteeism, and turnover.