A Modern Introduction to Probability and Statistics

Dekking, F. M.; Kraaikamp, Cor; Lopuhaä, Hendrik P.; Meester, Ludolf Erwin

doi:10.1007/1-84628-168-7

Cited by 418 publications

(258 citation statements)

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“…Bias and variability With respect to point (b), it is important to note that the accuracy with which a parameter is estimated is typically evaluated with respect to two criteria: variability and bias (Dekking, Kraaikamp, Lopuhaa, & Meester, 2005). Interpreting variability (i.e., efficiency) and bias concurrently, however, can be difficult.…”

Section: Overview Of Analytic Strategy and Interpretation Of Figuresmentioning

confidence: 99%

Examining workgroup diversity effects: does playing by the (group-retention) rules help or hinder?

et al. 2011

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Group diversity researchers are often faced with the problem of calculating diversity indices for groups that are incomplete due to participant nonresponse. Because participant nonresponse may attenuate the correlations that are observed between group diversity scores and outcome variables, some researchers use group-retention rules based on within-group response rates. With this approach, only those groups that have a within-group response rate at, or higher than, the rate prescribed by the group-retention rule are retained for subsequent analyses. We conducted two sets of experiments using computer simulations to determine the usefulness of group-retention rules. We found that groupretention rules are not a substitute for a high response rate and may decrease the accuracy of observed relations, and consequently, we advise against their use in diversity research.Keywords Missing data . Diversity . Groups . Retention rulesOver the past two decades, researchers and practitioners have shown considerable interest in understanding the impact that workgroup diversity has on group-level process variables (e.g., conflict, communication) and outcome variables (e.g., viability, innovation, performance). Some theoretical perspectives, such as the information-processing/decision-making approach (Ancona & Caldwell, 1992;De Dreu & West, 2001;Gruenfeld, Mannix, Williams, & Neale, 1996), predict that diversity will enhance processes and outcomes, whereas others, in particular the social identity, self-categorization, and similarity attraction perspectives (e.g., Byrne, 1971;Hogg & Abrams, 1988;Tajfel & Turner, 1986), predict that diversity will disrupt processes and impede outcomes. As various reviewers have concluded, the overall pattern of findings examining the effects of diversity is quite mixed (e.g., Horwitz & Horwitz, 2007;King, Hebl, & Beal, 2009;Mannix & Neale, 2005;Riordan, 2001;van Knippenberg, De Dreu, & Homan, 2004;Williams & O'Reilly, 1998). In their review, Mannix and Neale argued that these mixed results will only be clarified "by carefully considering moderators such as context, by broadening our view to include new types of diversity such as emotions and networks, and by focusing more carefully on mediating mechanisms" (p. 32). More recently, King et al. expressed a similar sentiment and emphasized the need, in future diversity research, to examine group processes and outcomes with greater specificity.We agree with these observations, but would add that methodological issues associated with diversity research also need careful scrutiny. Specifically, we contend that clarifying the mixed results within the diversity literature requires attention to how, and from whom, researchers collect the data on which diversity variables are based (e.g., Riordan, 2001), how well researchers' conceptualizations of diversity match their operationalizations of diversity (e.g., Harrison & Klein, 2007), and how researchers handle the ubiquitous missing-data problem that hampers the accurate assessment of workgroup dive...

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Section: Overview Of Analytic Strategy and Interpretation Of Figuresmentioning

confidence: 99%

Examining workgroup diversity effects: does playing by the (group-retention) rules help or hinder?

et al. 2011

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“…, n e . By using the property of the normal distribution that sum of the independent normally distributed random variables has also a normal distribution (Dekking et al, 2005), we conclude that the random variable…”

Section: Approximation Methodsmentioning

confidence: 96%

An Approximation Approach for Identification of Stochastic Max-Plus Linear Systems

Farahani

Boom

Schutter

2011

IFAC Proceedings Volumes

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Max-plus linear systems belong to a special class of discrete-event systems that consists of systems with synchronization but no choice. Our focus in this paper is on stochastic max-plus linear systems, i.e., perturbed systems that are linear in the max-plus algebra. One interesting topic is the identification of such systems. Previous works report on a method for identifying the parameters of a state space model for a stochastic max-plus linear system from measured data. However, due to the structure of such systems, this method results in a complex identification problem. Therefore, the aim of this paper is to decrease the computational complexity and the computation time of this problem. To this end, we use an approximation approach that is based on the higher-order raw moments of a random variable. This method results in a less complex problem that can be solved efficiently using gradient search techniques.

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“…Accordingly, the computation of (16) is straightforward, and we only need to elaborate on the computation of (17). By using the property of the normal distribution that sum of the independent normally distributed random variables also has a normal distribution [29], we conclude that the random variable (16) is also normally distributed with mean (16) is then nothing but the p-th raw moment of a normally distributed random variable, which is by definition equal to…”

Section: Approximation Methodsmentioning

confidence: 99%

Model predictive control for stochastic max-min-plus-scaling systems - an approximation approach

Farahani

Boom

Schutter

2011

IEEE Conference on Decision and Control and European Control Conference

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If you want to cite this report, please use the following reference instead:S.S. Farahani, T. van den Boom, and B. De Schutter, "Model predictive control for stochastic max-min-plus-scaling systems -An approximation approach," Proceedings of the 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), Orlando, Florida, pp. 391-396, Dec. 2011 Abstract-A large class of discrete-event and hybrid systems can be described by a max-min-plus-scaling (MMPS) model, i.e., a model in which the main operations are maximization, minimization, addition, and scalar multiplication. Further, Model Predictive Control (MPC), which is one of the most widely used advanced control design methods in the process industry due to its ability to handle constraints on both inputs and outputs, has already been extended to both deterministic and stochastic MMPS systems. However, in order to compute an MPC controller for a general MMPS system, a nonlinear, nonconvex optimization problem has to be solved. In addition, for stochastic MMPS systems, the problem is computationally highly complex since the cost function is defined as the expected value of an MMPS function and its evaluation leads to a complex numerical integration. The aim of this paper is to decrease this computational complexity by applying an approximation method that is based on the raw moments of a random variable, to a stochastic MMPS system with a Gaussian noise. In this way, the problem can be transformed into a sequence of convex optimization problems, providing that linear or convex MPC input constraints are considered.

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A Modern Introduction to Probability and Statistics

Cited by 418 publications

References 0 publications

Examining workgroup diversity effects: does playing by the (group-retention) rules help or hinder?

Examining workgroup diversity effects: does playing by the (group-retention) rules help or hinder?

An Approximation Approach for Identification of Stochastic Max-Plus Linear Systems

Model predictive control for stochastic max-min-plus-scaling systems - an approximation approach

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