Two machine scheduling problem when passing allowed

Bagga [1967] andJohnson [1954] developed algorithms for finding an optimum sequence, whenn jobs require processing on two machines. The criteria chosen by them were minimum waiting time of jobs and minimum elapsed time respectively. All then jobs were assumed to be of the same type i.e., all have to be processed on two machinesA andB in the orderAB. This paper presents a procedure for finding the optimal schedule of the jobs, when the jobs may be of the following types:

1. Jobs which are to be processed on only one of the machines.
2. Jobs which require processing on the two machinesA andB in the orderAB; and
3. Jobs which require processing on the two machinesA andB in the orderBA.

     

Optimality and construction of some rectangular designs

We obtain a sufficient condition forE-optimality of equireplicate designs. As an application, we proveE-optimality of certain types of three-class PBIBDs based on rectangular association scheme — in short — rectangular designs. These designs turn out to be highly efficient with respect to theA-criterion as well. We also observe that these designs, though themeselves not regular graph designs (RGD's) are yet strictlyE-better than every competing RGD, wheneverv≥26 andv=2 (mod 4). This provides an infinite series of counter examples to the conjecture of John and Mitchell (1977). We also present two methods of construction of the rectangular designs. Apart from providing infinitely many examples of the designs provedE-optimal in this paper and in Cheng and Constantine (1986), this construction also provides — as a special case — the first known infinite series of most balanced group divisible designs, which were proved optimal with respect to all type 1 criteria by Cheng (1978).     

A note on R-optimal designs for multiresponse models

This paper considers the optimal design problem for multiresponse regression models. The $R$ -optimality introduced by Dette (J R Stat Soc B 59:97–110, 1997) for single response experiments is extended to the case of multiresponse parameter estimation. A general equivalence theorem for the $R$ -optimality is provided for multiresponse models. Illustrative examples of the $R$ -optimal designs for two multiresponse models are presented based on the general equivalence theorem.     

R-optimal designs for multi-factor models with heteroscedastic errors

In this paper, we consider the R-optimal design problem for multi-factor regression models with heteroscedastic errors. It is shown that a R-optimal design for the heteroscedastic Kronecker product model is given by the product of the R-optimal designs for the marginal one-factor models. However, R-optimal designs for the additive models can be constructed from R-optimal designs for the one-factor models only if sufficient conditions are satisfied. Several examples are presented to illustrate and check optimal designs based on R-optimality criterion.     

Su di una politica di bonus

Si prende in considerazione una politica di Bonus attuata da una Compagnia di assicurazione e si studia l'andamento della perdita attesa della Compagnia al variare dell'entità del Bonus nei casi in cui:

1. la funzioneL(s), che esprime il livello minimo dell'entità dei sinistri non occultabili all'istantes, sia lineare ins;
2. la funzioneL(s) renda massima la previsione del guadagno di ogni singolo assicurato.

     

Self-reflexive networks: Theory · topology · applications

We present a new type of Artificial Neural Networks: the Self-Reflexive Networks. We utter the theoretical presuppositions; their dynamics is analogous to the one ascribed to autopoietic systems: self-referentiality, unsupervised learning and unintentionally cooperative and contractual activities of their own units. We also hypothesize a new concept of perception. We present the basic equations of Self-Reflexive Networks, new concepts as the one of dynamic target, of Re-entry with dedicated and fixed connections, of Meta-Units. Therefore, we experiment a specific type of Self-Reflexive Networks, the Monodedicated, within the interpretation of a toy-DB and we have hinted at other already made experimentations, experimentations in process and planned experimentations. From the applicative work that we present a few specifics and novelties of this type of Neural Networks emerge:

1. the capability of answering to complex, strange, wrong or not precise questions, through the same algorithms through which the learning phase took place.
2. the capability of spontaneously transforming their own learning inaccuracy in analogic capability and original self-organization capability.
3. the capability of spontaneously integrate the models that it experienced in different moments in an achronical hyper-model.
4. the capability of behaving as it had explored a decisions graph of large dimensions, both deeply and in extension. With the consequence of behaving as an Addressing Memory forself-dynamic Contents.
5. the capability of always learning, rapidly and anyway, besides the complexity of the learning patterns.
6. the capability of answering simultaneously from different points of view, behaving, in this case, as a network that builds more similarity models for each vector-stimulus that it receives.
7. the capability of adjusting in a biunivocal way, each question to the consulting DB and each DB to the question that are submitted. The consequence of this fact is the continuous creation of new answering models.
8. the capability of building during the learning phase, a weights matrix that provides a subconceptual representation of the bi-directional relations between each couple of input variables.
9. the capability, through the Metaunits, to integrate in a unitary typology, nodes with different saturation speed and, therefore, with different memory: in fact, while the SR units are short memory nodes, since each new stumulus zeros the previous stimulus, the Metaunits memorize the SR different stimulus during time, functioning as an average length memory. This fact should confirm that the avarage length memory is of a different level from the immediate memory and that it is based only uponrelation among perceptive stimulus which are distributed in parallel and in sequence. In this context the weights matrix constitute the SR long term memory. And in this sense it will be opportune to think at a methodic through which the Metaunits can influence during time, the same weights matrix. In any case, in the SR there areservice nodes orfilter nodes andlearning nodes as if they were weights (the Metaunits).

     

Una nota su operazioni associative,trasformate integrali e problemi di caratterizzazione in statistica

In questa nota si studia una equazione funzionale che ha interessanti applicazioni al Calcolo delle Probabilità. Si illustrano due possibilità applicative:

1. la scelta del nucleo delle trasformate integrali;
2. la caratterizzazione di opportune classi di variabili casuali.

     

Conditions for the optimality of exponential smoothing forecast procedures

Exponential smoothing procedures, in particular those recommended byBrown [1962] are used extensively in many areas of economics, business and engineering. It is shown in this paper that:

1. Brown's forecasting procedures are optimal in terms of achieving minimum mean square error forecasts only if the underlying stochastic process is included in a limited subclass of ARIMA (p, d, q) processes. Hence, it is shown what assumptions are made when using these procedures.
2. The implication of point (i) is that the users ofBrown's procedures tacitly assume that the stochastic processes which occur in the real world are from the particular restricted subclass of ARIMA (p, d, q) processes. No reason can be found why these particular models should occur more frequently than others.
3. It is further shown that even if a stochastic process which would lead toBrown's model occurred, the actual methods used for making the forecasts are clumsy and much simpler procedures can be employed.

     

Verso un unico tipo di concavità

La classica disuguaglianza che definisce la concavità di una funzione: $$f(\alpha x + \bar \alpha y) \geqq \alpha f(x) + \bar \alpha f(y){\mathbf{ }}\bar \alpha = 1 - \alpha$$ può essere generalizzata in due modi:

1. ammettendo pesi diversi nei due membri;
2. ammettendo medie diverse dall'aritmetica.

In questo lavoro si mostra come queste due estensioni contengano vari tipi di concavità generalizzata proposti in letteratura: quasi concavità, pseudoconcavità, concavità forte, “concavificabilità”. Le due estensioni citate si possono per di più riunire in un'unica che prevede sia pesi eventualmente diversi nei due membri sia medie non necessariamente artimetiche. Presentiamo anche alcune semplici proprietà di queste funzioni concave generalizzate.     

D-optimal chemical balance weighing designs with autoregressive errors

In this paper, we consider the estimation problem of individual weights of three objects. For the estimation we use the chemical balance weighing design and the criterion of D-optimality. We assume that the error terms ${\varepsilon_{i},\ i=1,2,\dots,n,}$ are a first-order autoregressive process. This assumption implies that the covariance matrix of errors depends on the known parameter ρ. We present the chemical balance weighing design matrix ${\widetilde{\bf X}}$ and we prove that this design is D-optimal in certain classes of designs for ${\rho\in[0,1)}$ and it is also D-optimal in the class of designs with the design matrix ${{\bf X} \in M_{n\times 3}(\pm 1)}$ for some ρ ≥ 0. We prove also the necessary and sufficient conditions under which the design is D-optimal in the class of designs ${M_{n\times 3}(\pm 1)}$ , if ${\rho\in[0,1/(n-2))}$ . We present also the matrix of the D-optimal factorial design with 3 two-level factors.     

Some results on constructing general minimum lower order confounding 2^{n-m} designs for n\le 2^{n-m-2}

Zhang et al. (Stat Sinica 18:1689–1705, 2008) introduced an aliased effect-number pattern for two-level regular designs and proposed a general minimum lower-order confounding (GMC) criterion for choosing optimal designs. All the GMC \(2^{n-m}\) designs with \(N/4+1\le n\le N-1\) were constructed by Li et al. (Stat Sinica 21:1571–1589, 2011), Zhang and Cheng (J Stat Plan Inference 140:1719–1730, 2010) and Cheng and Zhang (J Stat Plan Inference 140:2384–2394, 2010), where \(N=2^{n-m}\) is run number and \(n\) is factor number. In this paper, we first study some further properties of GMC design, then we construct all the GMC \(2^{n-m}\) designs respectively with the three parameter cases of \(n\le N-1\) : (i) \(m\le 4\) , (ii) \(m\ge 5\) and \(n=(2^m-1)u+r\) for \(u>0\) and \(r=0,1,2\) , and (iii) \(m\ge 5\) and \(n=(2^m-1)u+r\) for \(u\ge 0\) and \(r=2^m-3,2^m-2\) .     

Consistency of a nonparametric estimation of a density functional

LetX be a random variable with distribution functionF and density functionf. Let ? and ψ be known measurable functions defined on the real lineR and the closed interval [0, 1], respectively. This paper proposes a smooth nonparametric estimate of the density functional \(\theta = \int\limits_R \phi (x) \psi \left[ {F (x)} \right]f^2 (x) dx\) based on a random sampleX ₁, ...,X _n fromF using a kernel functionk. The proposed estimate is given by \(\hat \theta = (n^2 a_n )^{ - 1} \mathop \sum \limits_{i = 1}^n \mathop \sum \limits_{j = 1}^n \phi (X_i ) \psi \left[ {\hat F (X_i )} \right]k\left[ {(X_i - X_j )/a_n } \right]\) , where \(\hat F(x) = n^{ - 1} \mathop \sum \limits_{i = 1}^n K\left[ {(x - X_i )/a_n } \right]\) with \(K (w) = \int\limits_{ - \infty }^w {k (u) } du\) . The estimate \(\hat \theta \) is shown to be consistent both in the weak and strong sense and is used to estimate the asymptotic relative efficiency of various nonparametric tests, with particular reference to those using the Chernoff-Savage statistic.     

Sul ruolo dei riassunti esaustivi ai fini della previsione in contesto Bayesiano (2a parte)

Proseguendo nella ricerca, la cui prima parte è stata pubblicata nel precedente numero di questa Rivista, trattiamo delle successioni di riassunti esaustivi a fini preditivi (r.e.f.p.) ed affrontiamo i seguenti problemi:

1. assegnata una distribuzione predittiva per cui esiste un r.e.f.p. è possibile individuare un modello ipotetico con essa compatibile?
2. in caso affermativo, qual è il collegamento tra tale r.e.f.p. e gli eventuali r.e. in senso classico relativi al modello ipotetico individuato?

Infine analizzeremo, in modo piuttosto informale e perciò ulteriormente sviluppabile, il problema dell'espressione analitica di un r.e.f.p.     

Inference robustness of ARIMA models under non-normality —Special application to stock price data

Wold's decomposition theorem [Wold] states that every weakly stationary stochastic process can be written as a linear combination of orthogonal shocks. For practical reasons, however, it is desirable to employ models which use parameters parsimoniously.Box andJenkins [1970] show how parsimony can be achieved by representing the linear process in terms of a small number of autoregressive and moving average terms (ARIMA-models). The Gaussian hypothesis assumes that the shocks follow a normal distribution with fixed mean and variance. In this case the process is characterized by first and second order moments. The normality assumption seems reasonable for many kinds of series. However, it was pointed out byKendall [1953],Mandelbrot [1963, 1967],Fama [1965],Mandelbrot andTaylor [1967] that particularly for stock price data the distribution of the shocks appears leptokurtic: In this paper we investigate the sensitivity of ARIMA models to non-normality of the distribution of the shocks. We suppose that the distribution function of the shocks is a member of the symmetric exponential power family, which includes the normal as well as leptokurtic and platikurtic distributions. A Bayesian approach is adopted and the inference robustness of ARIMA models with respect to

1. the estimation of parameters
2. the forecasts of future observations is discussed.

     

Dispersive ordering of fail-safe systems with heterogeneous exponential components

Let X ₁, . . . , X _n be independent exponential random variables with respective hazard rates λ₁, . . . , λ _n , and Y ₁, . . . , Y _n be independent and identically distributed random variables from an exponential distribution with hazard rate λ. Then, we prove that X _2:n , the second order statistic from X ₁, . . . , X _n , is larger than Y _2:n , the second order statistic from Y ₁, . . . , Y _n , in terms of the dispersive order if and only if

$\lambda\geq \sqrt{\frac{1}{{n\choose 2}}\sum_{1\leq i < j\leq n}\lambda_i\lambda_j}.$

We also show that X _2:n is smaller than Y _2:n in terms of the dispersive order if and only if

$ \lambda\le\frac{\sum^{n}_{i=1} \lambda_i-{\rm max}_{1\leq i\leq n} \lambda_i}{n-1}. $

Moreover, we extend the above two results to the proportional hazard rates model. These two results established here form nice extensions of the corresponding results on hazard rate, likelihood ratio, and MRL orderings established recently by Pǎltǎnea (J Stat Plan Inference 138:1993–1997, 2008), Zhao et al. (J Multivar Anal 100:952–962, 2009), and Zhao and Balakrishnan (J Stat Plan Inference 139:3027–3037, 2009), respectively.

     

Minimal efficiency of designs under the class of orthogonally invariant information criteria

Consider the linear regression model with uncorrelated errors and an experimental design . In the article, we address the problem of calculating the minimal efficiency of with respect to the class of orthogonally invariant information criteria, containing all Kiefers criteria of _p-optimality, among others. We show that the -minimal efficiency of is equal to the minimal efficiency of with respect to a finite class of criteria which generalize the criterion of E-optimality. We also formulate conditions under which a design is maximin efficient, i.e. the most efficiency-stable for criteria from . To illustrate the results, we calculated the -minimal efficiency of _p (in particular D, A and E) optimal designs for polynomial regression on [–1,1] up to degree 4. Moreover, for the quadratic model we explicitly constructed the -maximin efficient design.Acknowledgement. The author would like to thank prof. Pázman as well as an anonymous referee for useful and inspiring comments on earlier versions of this article.Supported by: VEGA grant of the Slovak Agency No. 1/0264/03     

Totally positive regression: E-optimal designs

E-optimality of approximate designs in linear regression models is paired with a dual problem of nonlinear Chebyshev approximation. When the regression functions form a totally positive system, then the information matrices of designs for subparameters turn out to be “almost” totally positive, a property which allows to solve the nonlinear Chebyshev problem. Thereby we obtain explicit formulae for E-optimal designs in terms of equi-oscillating generalized polynomials. The considerations unify and generalize known results on E-optimality for particular regression setups.     

An extremal property of the generalized arcsine distribution

The main result of the paper is the following characterization of the generalized arcsine density p _γ (t) = t ^γ?1(1 ? t) ^γ?1/B(γ, γ)   with ${t \in (0, 1)}$ and ${\gamma \in(0,\frac12) \cup (\frac12,1)}$ : a r.v. ξ supported on [0, 1] has the generalized arcsine density p _γ (t) if and only if ${ {\mathbb E} |\xi- x|^{1-2 \gamma}}$ has the same value for almost all ${x \in (0,1)}$ . Moreover, the measure with density p _γ (t) is a unique minimizer (in the space of all probability measures μ supported on (0, 1)) of the double expectation ${ (\gamma-\frac12 ) {\mathbb E} |\xi-\xi^{\prime}|^{1-2 \gamma}}$ , where ξ and ξ′ are independent random variables distributed according to the measure μ. These results extend recent results characterizing the standard arcsine density (the case ${\gamma=\frac12}$ ).