Robustness checks using linear probability models
Bayesian Inference: Probit and Linear Probability Models
... expenditures. Using the center for Research on Security Prices(CRSP), the sample size was extended to include all firms with the Standard Industrial Classification Codes (SIC) 60, 61, and ...
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Estimating the Probability of Earthquake Occurrence and Return Period Using Generalized Linear Models
... estimated probability of an earthquake occurrence and return period are statistical predicted values, cal- culated from a set of earthquake data of ...the probability of exceedance in 50 years and 100 years ...
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CAS: Estimation and Robustness of Linear Mixed Models in Credibility Context
... Furthermore, from the empirical studies, the time our approach takes is much shorter than the GEE approach. For instance, it takes less than 15 minutes to get the ML and REML estima- tion results for 500 repetitions in ...
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Robustness of zero augmented models over generalized linear models in analysing fertility data in Nigeria
... results using the AIC and BIC has a model selec- tion reviewed that both hurdle negative binomial and zero-inflated negative binomial provide a better fit for fertility data with large number of zeros and ...
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State-Dependent Probability Distributions in Non Linear Rational Expectations Models
... Finally, we apply our method to a Fisherian model of inflation determination in which the monetary policy rule may change across regimes according to a state-dependent jump process. We find similar conditions to Farmer ...
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Balancing flexibility and robustness in machine learning: semi-parametric methods and sparse linear models
... attained using standard parametric or purely non-parametric approaches in ...accurate models for the ...semi-parametric models to describe arbitrary non-linear dependencies between two random ...
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System design of stochastic models using robustness of temporal properties
... the probability of the formula statistically, for model parameters as in Table 1 and formula pa- rameters T 1 = 10 and T 2 = 15, then we obtain the value p = ...the robustness degree of the formula, see ...
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Robustness against Relaxed Memory Models
... 7.3 using a 4-core machine equipped with Intel(R) Core(TM) i5 CPU M 560 @ ...reachability checks that returned a negative ...of checks that showed that the goal state is ...
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Fit Generalized Linear Models by Using of Different Likelihoods
... The complexity of the model is determined by the number of parameters in it. The likelihood function is in the form of ( ) ( ) L P y , where P y ( ) is the probability of the observed data. When the ...
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Fitting Linear Mixed Models Using Linked Data
... In addition, it is assumed that the linked records can be partitioned into Q distinct blocks such that there is no possibility that linked records in different blocks contain data for the same population unit. This ...
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Probability density estimation in stochastic environmental models using reverse representations
... variance linear with the number of forward and reverse realizations (we assume N = M from now, unless specified otherwise) infinitely, and that such increase will affect the computation time ...
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On the Robustness of Robustness Checks of the Environmental Kuznets Curve
... the Robustness of Robustness Checks of the Environmental Kuznets Curve Summary Since its first inception in the debate on the relationship between environment and growth in 1992, the Environmental ...
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Linear location estimators : the dependence of their quality on the shape of the probability density function, and their robustness
... s·tudents-t distribution with 6 degrees of edom have the same kurtosis but are best estimated di rently; D'Agostino and Lee (1977) have shown that the asymptotic[r] ...
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Customer Management Using Probability Models (a.k.a. Stochastic Forecasting Models)
... powerful models that use readily available purchasing data to capture underlying patterns in customer ...these models to provide accurate forecasts for what these customers will do in the ...
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Linear probability model, s.e ;
... Covariates control for the ex ante internal risk rating (0–5 range, with higher a better rating); the (squared) length of the firm-bank relationship (the number o[r] ...
Robustness and Visualization of Decision Models
... correlated. Linear combinations of these original variables define a set of principal components forming the unique non-redundant orthogonal basis of a new space of ...
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On the Robustness of Self Attentive Models
... two models after changing one word in ...self-attentive models, the distribution of change on embeddings is sparse after going through the first self-attention layer (layer 1) and then gradually propagate ...
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Robustness in Latent Variable Models
... Under the scenario and parameter settings in Example 2.2, we examined operating characteristics of T 1,1 ∗ and T 1,2 ∗ via a Monte Carlo simulation study. In the simulation, 100 data sets of size n = 1000 were generated ...
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Probability of Default Modeling for a Bank Using Scoring Models
... Tato položka patří mezi hlavní, které rozhodují o tom, jak bude výsledné z –score vypadat, neboť dokonce až u dvou bank stojí na prvním místě v pořadí vlivů a jde o banky: HB a RB, u d[r] ...
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Ultralogics and probability models
... ROBERT A. HERRMANN (Received 9 December 2000) Abstract. We show how nonstandard consequence operators, ultralogics, can generate the general informational content displayed by probability models. In ...
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