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Table 2 Simulated data example

From: A flexible distribution class for count data

  

Simulated data distribution

  

Bin(b=3, p =0.667)

Pois(μ =6)

NB(n=3, p=0.333)

 

\(\hat {\mu }_{*}\) (SE)

2.0000(0.1414)

6.1100(0.2472)

5.3000(0.2302)

Pois.

log(L)

-144.8109

-228.1811

-288.0710

 

AIC

291.6218

458.3623

578.1419

 

BIC

294.2270

460.9675

580.7471

 

\(\hat {\mu }\) (SE)

1.9999(0.1419)

6.1100(0.2503)

5.3001(0.3632)

 

\(\hat {\theta }\) (SE)

276.5396(394.3489)

239.7812(421.0122)

3.5599(0.8564)

NB

log(L)

-145.0563

-228.3236

-258.7486

 

AIC

294.1126

460.6472

521.4971

 

BIC

299.3229

465.8575

526.7075

 

\(\hat {\lambda }\) (SE)

18.7071(8.9855)

6.9145(2.1193)

1.5576(0.2079)

 

\(\hat {\nu }\) (SE)

3.3931(0.5024)

1.0653(0.1603)

0.3150(0.0708)

CMP/sCMP(m=1)

log(L)

-123.2624

-228.0950

-260.3649

 

AIC

250.5248

460.1900

524.7298

 

BIC

255.7351

465.4003

529.9402

 

\(\hat {\lambda }\) (SE)

4.2531(0.9494)

3.4046(0.8152)

0.9309(0.1109)

 

\(\hat {\nu }\) (SE)

4.2854(0.4998)

1.0838(0.1826)

0.1674(0.0825)

sCMP(m=2)

log(L)

-120.8816

-228.0722

-259.3193

 

AIC

245.7632

460.1444

522.6386

 

BIC

250.9735

465.3547

527.8489

 

\(\hat {\lambda }\) (SE)

2.0000(0.2450)

2.2683(0.4822)

0.6709(0.0576)

 

\(\hat {\nu }\) (SE)

33.6942(12536.57)

1.1093(0.2127)

0.0392(0.0698)

sCMP(m=3)

log(L)

-116.2486

-228.0418

-258.8683

 

AIC

236.4972

460.0836

521.7366

 

BIC

241.7075

465.2939

526.9469

 

\(\hat {\lambda }\) (SE)

1.0000(0.1000)

1.7044(0.3382)

0.5700(0.0486)

 

\(\hat {\nu }\) (SE)

32.5126(12322.57)

1.1381(0.2469)

0.0000(0.0826)

sCMP(m=4)

log(L)

-124.7123

-228.0175

-258.8470

 

AIC

253.4246

460.0350

521.6940

 

BIC

258.6349

465.2453

526.9043

  1. True model parameters versus estimated parameters (and associated standard errors provided in parentheses) for various assumed distributions. For model comparisons, the log-likelihood, Akaike and Bayes Information Criterions (AIC and BIC, respectively) are provided