Multibrand Transition Probabilities as a Function of Explanatory Variables: Estimation by a Least-Squares-Based Approach

1986 ◽  
Vol 23 (2) ◽  
pp. 177-183 ◽  
Author(s):  
Fred S. Zufryden
Keyword(s):  
Least Squares ◽  
Goodness Of Fit ◽  
Transition Probabilities ◽  
Weighted Least Squares ◽  
Estimation Procedure ◽  
Explanatory Variables ◽  
First Order ◽  
Proposed Model ◽  
Markov Transition ◽  
The Relationship

A model is formulated to express the relationship between first-order Markov transition probabilities for a multibrand market and explanatory variables. The author shows that the parameters of the model can be estimated through a proposed restricted weighted least squares procedure. An empirical implementation of the estimation procedure illustrates the structure, goodness of fit, and predictive validity of the proposed model.

scholarly journals An Approach Using Adaptive Weighted Least Squares Support Vector Machines Coupled with Modified Ant Lion Optimizer for Dam Deformation Prediction

2020 ◽  
Vol 2020 ◽  
pp. 1-23 ◽  
Author(s):  
Yijun Chen ◽  
Chongshi Gu ◽  
Chenfei Shao ◽  
Hao Gu ◽  
Dongjian Zheng ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Weighted Least Squares ◽  
Support Vector ◽  
Deformation Prediction ◽  
Ant Lion Optimizer ◽  
Proposed Model ◽  
Vector Machines ◽  
Ant Lion ◽  
Dam Deformation

A dam deformation prediction model based on adaptive weighted least squares support vector machines (AWLSSVM) coupled with modified Ant Lion Optimization (ALO) is proposed, which can be utilized to evaluate the operational states of concrete dams. First, the Ant Lion Optimizer, a novel metaheuristic algorithm, is used to determine the punishment factor and kernel width in the least squares support vector machine (LSSVM) model, which simulates the hunting process of antlions in nature. Second, aiming to solve the premature convergence phenomenon, Levy flight is introduced into the ALO to improve the global optimization ability. Third, according to the statistical characteristics of the datum error, an improved normal distribution weighting rule is applied to update the weighted value of data samples based on the learning result of the LSSVM model. Moreover, taking a concrete arch dam in China as an example, the horizontal displacement recorded by a pendulum is used as a study object. The accuracy and validity of the proposed model are verified and evaluated based on the four evaluating criteria, and the results of the proposed model are compared with those of well-established models. The simulation results demonstrate that the proposed model outperforms other models and effectively overcomes the influence of outliers on the performance of the model. It also has high prediction accuracy, produces excellent generalization performance, and can be a promising alternative technique for the analysis and prediction of dam deformation and other fields, including flood interval prediction, the stock price market, and wind speed forecasting.

The Relationship between Hardiness, Supervisor Support, Group Cohesion, and Job Stress as Predictors of Job Satisfaction

2003 ◽  
Vol 17 (6) ◽  
pp. 382-389 ◽  
Author(s):  
Mary A. Steinhardt ◽  
Christyn L. Dolbier ◽  
Nell H. Gottlieb ◽  
Katherine T. McCalister
Keyword(s):  
Job Satisfaction ◽  
Job Stress ◽  
Support Group ◽  
Goodness Of Fit ◽  
Group Cohesion ◽  
Supervisor Support ◽  
Full Time ◽  
Convenience Sample ◽  
Proposed Model ◽  
The Relationship

Purpose. This study tested a conceptual model based on research supporting the relationship between the predictors of hardiness, supervisor support, and group cohesion and the criterions of job stress and job satisfaction and between the predictor of job stress and the criterion of job satisfaction. Design. The study employed a cross-sectional research design. Survey data were collected as part of the baseline measures assessed prior to an organizational hardiness intervention. Setting. Worksite of Dell Computer Corporation in Austin, Texas. Subjects. The subjects included 160 full-time Dell employees recruited from a convenience sample representing nine work groups (response rate = 90%). Measures. Hardiness was measured using the Dispositional Resilience Scale (DRS), job stress was measured using the Perceived Work Stress Scale (PWSS), and supervisor support, group cohesion, and job satisfaction were measured using a proprietary employee attitude survey. Results. In the proposed model, high hardiness, supervisor support, and group cohesion were related to lower levels of job stress, which in turn was related to higher levels of job satisfaction. The model also proposed direct paths from hardiness, supervisor support, and group cohesion to job satisfaction. Path analysis was used to examine the goodness of fit of the model. The proposed model was a good fit for the data (χ2[1, N = 160] = 1.85, p = .174) with the exception of the direct path between group cohesion and job satisfaction. Substantial portions of the variances in job stress ( R2 = .19) and job satisfaction (R2 = .44) were accounted for by the predictors. Conclusion. Implications for targeted worksite health promotion efforts to lower job stress and enhance job satisfaction are discussed.

scholarly journals Pemodelan Persamaan Struktural: Penerapan Manajemen Pengetahuan Dan Faktor-Faktor Pendukungnya

2017 ◽  
Vol 18 (2) ◽  
pp. 159
Author(s):  
Farid Wajdi ◽  
RB. Tri Joko Wibowo
Keyword(s):  
Knowledge Management ◽  
Goodness Of Fit ◽  
Organizational Structures ◽  
Technology Infrastructure ◽  
Chi Square ◽  
Leadership Culture ◽  
Implementation Model ◽  
Proposed Model ◽  
Cultural Mediators ◽  
The Relationship

The application of knowledge management in an organization requires the enabler factors that should be identified of its readiness before implementation. This study examined knowledge management implementation model with the variables of organizational leadership, culture, structure as a predictor of technology infrastructure variable. The purpose of this study is to examine the fitness of the proposed model with data in the field, as well as to know the relationship between these factors. Data were collected through questionnaire using likert scale 1-5 on 112 respondents from a manufacturing company in Cilegon, Banten. Modeling was done using residual correlation and regression of knowledge sharing indicators and indicator of KM team function showed the appropriate result but not yet reached the expected value of Chi-Square that is 73.502 with p = 0.010, GFI = 0.907, RMSEA = 0.069, AGFI = 0.848, and RMR = 0.041. Overall the modified model shows results that match the criteria of Goodness of Fit. The results of model analysis also show that there is an indirect influence of leadership variables on technology infrastructure, through cultural mediators and organizational structures, but in the model does not explain the direct influence of leadership on technology infrastructure.

Factor Structure and Invariance of the POMS Mood State Questionnaire in Spanish

2010 ◽  
Vol 13 (1) ◽  
pp. 444-452 ◽  
Author(s):  
Elena Andrade ◽  
Constantino Arce ◽  
Julio Torrado ◽  
Javier Garrido ◽  
Cristina De Francisco ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Mood State ◽  
Measurement Model ◽  
Original Form ◽  
Fit Indices ◽  
Data Set ◽  
First Order ◽  
Proposed Model ◽  
Confirmatory Factor ◽  
Sample Set

The purpose of this study was to examine the extent to which the Spanish POMS assesses the same factors as the original form of the questionnaire. We started from a version with 63 items, representing seven conceptual dimensions. This version was administered to a sample of 364 adult athletes. In the whole sample, exploratory factor analytic findings suggested a more parsimonious measurement model, with 44 items and 6 first-order factors. Then the data from said sample were randomly divided into two sets, each containing about 50% of the subjects. The fit of the first sample set (n = 166) to the proposed model was adequate. Four of the main goodness-of-fit indices exhibited the following values: CFI = .95, NNFI = .95, SRMR = .083, and RMSEA = .064. We tested the same model in the second data set (n = 198), in which the fit was also acceptable, with values of .95, .94, .088, and .066 for CFI, NNFI, SRMR, and RMSEA, respectively. In addition, we used multi-group confirmatory factor analysis to provide evidence on the invariance of the model.

All Events Induce Variance: Analyzing Abnormal Returns When Effects Vary across Firms

2007 ◽  
Vol 42 (1) ◽  
pp. 229-256 ◽  
Author(s):  
Scott E. Harrington ◽  
David G. Shrider
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Abnormal Returns ◽  
Event Studies ◽  
Standard Errors ◽  
Cross Sectional ◽  
Explanatory Variables ◽  
Robust Tests ◽  
Nonzero Mean ◽  
Short Horizon

AbstractWe demonstrate analytically that cross-sectional variation in the effects of events, i.e., in true abnormal returns, necessarily produces event-induced variance increases, biasing popular tests for mean abnormal returns in short-horizon event studies. We show that unexplained cross-sectional variation in true abnormal returns plausibly produces nonproportional heteroskedasticity in cross-sectional regressions, biasing coefficient standard errors for both ordinary and weighted least squares. Simulations highlight the resulting biases, the necessity of using tests robust to cross-sectional variation, and the power of robust tests, including regression-based tests for nonzero mean abnormal returns, which may increase power by conditioning on relevant explanatory variables.

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