diff --git a/lib/model/zip.js b/lib/model/zip.js
index afc68e9b1..ae90b5a3b 100644
--- a/lib/model/zip.js
+++ b/lib/model/zip.js
@@ -7,8 +7,11 @@ export default class ZeroInflatedPoisson {
 	// https://qiita.com/nozma/items/52211b1bacaa8a898164
 	// http://web.uvic.ca/~dgiles/downloads/count/zip.pdf
 	// https://ncss-wpengine.netdna-ssl.com/wp-content/themes/ncss/pdf/Procedures/NCSS/Zero-Inflated_Poisson_Regression.pdf
-	constructor() {
-		this._method = 'ml'
+	/**
+	 * @param {'moments' | 'maximum_likelihood'} [method=maximum_likelihood] Method name
+	 */
+	constructor(method = 'maximum_likelihood') {
+		this._method = method
 	}
 
 	/**
@@ -17,7 +20,7 @@ export default class ZeroInflatedPoisson {
 	 * @param {number[]} x Training data
 	 */
 	fit(x) {
-		if (this._method === 'mo') {
+		if (this._method === 'moments') {
 			this._mo(x)
 		} else {
 			this._ml(x)
diff --git a/tests/lib/model/zip.test.js b/tests/lib/model/zip.test.js
index 1de8fcedc..0397c2905 100644
--- a/tests/lib/model/zip.test.js
+++ b/tests/lib/model/zip.test.js
@@ -14,33 +14,31 @@ const random_poisson = l => {
 	return k - 1
 }
 
-test('density estimation', () => {
-	const model = new ZeroInflatedPoisson()
-	const x = []
-	for (let i = 0; i < 10000; i++) {
-		const r = Math.random()
-		if (r < 0.5) {
-			x.push(0)
-		} else {
-			x.push(random_poisson(1))
+describe('density estimation', () => {
+	test.each([undefined, 'moments', 'maximum_likelihood'])('%s', method => {
+		const model = new ZeroInflatedPoisson(method)
+		const x = []
+		for (let i = 0; i < 10000; i++) {
+			const r = Math.random()
+			x.push(r < 0.5 ? 0 : random_poisson(1))
 		}
-	}
 
-	model.fit(x)
+		model.fit(x)
 
-	const y = [0, 1, 2, 3, 4, 5]
-	const p = Array(y.length).fill(0)
-	p[0] += 0.5
-	for (let i = 0; i < y.length; i++) {
-		let f = 1
-		for (let k = 2; k <= i; k++) {
-			f *= k
+		const y = [0, 1, 2, 3, 4, 5]
+		const p = Array(y.length).fill(0)
+		p[0] += 0.5
+		for (let i = 0; i < y.length; i++) {
+			let f = 1
+			for (let k = 2; k <= i; k++) {
+				f *= k
+			}
+			p[i] += ((1 / f) * Math.exp(-1)) / 2
 		}
-		p[i] += ((1 / f) * Math.exp(-1)) / 2
-	}
 
-	const pred = model.probability(y)
-	for (let i = 0; i < y.length; i++) {
-		expect(pred[i]).toBeCloseTo(p[i])
-	}
+		const pred = model.probability(y)
+		for (let i = 0; i < y.length; i++) {
+			expect(pred[i]).toBeCloseTo(p[i])
+		}
+	})
 })